This page has only limited features, please log in for full access.
Background: Sex differences in neuromuscular fatigue is well-documented, however the underlying mechanisms remain understudied, particularly for the aging population. Objective: This study investigated sex differences in fatigability of the upper and lower extremity of older adults using a neuroergonomics approach. Methods: Thirty community-dwelling older adults (65 years or older; 15 M, 15 F) performed intermittent submaximal fatiguing handgrip and knee extension exercises until voluntary exhaustion on separate days. Muscle activity from prime muscles of the hand/arm and knee extensors were monitored using electromyography, neural activity from the frontal, motor, and sensory areas were monitored using functional near infrared spectroscopy, and force output were obtained. Results: While older males were stronger than females across both muscle groups, they exhibited longer endurance times and greater strength loss during knee extension exercises. These lower extremity findings were associated with greater force complexity over time and concomitant increase in left motor and right sensory motor regions. While fatigability during handgrip exercises was comparable across sexes, older females exhibited concurrent increases in the activation of the ipsilateral motor regions over time. Discussion: We identified differences in the underlying central neural strategies adopted by males and females in maintaining downstream motor outputs during handgrip fatigue that were not evident with traditional ergonomics measures. Additionally, enhanced neural activation in males during knee exercises that accompanied longer time to exhaustion point to potential rehabilitation/exercise strategies to improve neuromotor outcomes in more fatigable older adults.
Ranjana K. Mehta; Joohyun Rhee. Revealing Sex Differences During Upper and Lower Extremity Neuromuscular Fatigue in Older Adults Through a Neuroergonomics Approach. Frontiers in Neuroergonomics 2021, 2, 1 .
AMA StyleRanjana K. Mehta, Joohyun Rhee. Revealing Sex Differences During Upper and Lower Extremity Neuromuscular Fatigue in Older Adults Through a Neuroergonomics Approach. Frontiers in Neuroergonomics. 2021; 2 ():1.
Chicago/Turabian StyleRanjana K. Mehta; Joohyun Rhee. 2021. "Revealing Sex Differences During Upper and Lower Extremity Neuromuscular Fatigue in Older Adults Through a Neuroergonomics Approach." Frontiers in Neuroergonomics 2, no. : 1.
The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of
Maher Abujelala; Rohith Karthikeyan; Oshin Tyagi; Jing Du; Ranjana Mehta. Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics. Brain Sciences 2021, 11, 885 .
AMA StyleMaher Abujelala, Rohith Karthikeyan, Oshin Tyagi, Jing Du, Ranjana Mehta. Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics. Brain Sciences. 2021; 11 (7):885.
Chicago/Turabian StyleMaher Abujelala; Rohith Karthikeyan; Oshin Tyagi; Jing Du; Ranjana Mehta. 2021. "Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics." Brain Sciences 11, no. 7: 885.
Industrial passive low-back exoskeletons have gained recent attention as ergonomic interventions to manual handling tasks. This research utilized a two-armed experimental approach (single vs dual-task paradigms) to quantify neural and biomechanical tradeoffs associated with short-term human-exoskeleton interaction (HEI) during asymmetrical lifting in twelve healthy adults balanced by gender. A dynamic, electromyography-assisted spine model was employed that indicated statistical, but marginal, biomechanical benefits of the tested exoskeleton, which diminished with the introduction of the cognitive dual-task. Using Near Infrared Spectroscopy (fNIRS)-based brain connectivity analyses, we found that the tested exoskeleton imposed greater neurocognitive and motor adaptation efforts by engaging action monitoring and error processing brain networks. Collectively, these findings indicate that a wearer's biomechanical response to increased cognitive demands in the workplace may offset the mechanical advantages of exoskeletons. We also demonstrate the utility of ambulatory fNIRS to capture the neural cost of HEI without the need for elaborate dual-task manipulations.
Yibo Zhu; Eric B. Weston; Ranjana K. Mehta; William S. Marras. Neural and biomechanical tradeoffs associated with human-exoskeleton interactions. Applied Ergonomics 2021, 96, 103494 .
AMA StyleYibo Zhu, Eric B. Weston, Ranjana K. Mehta, William S. Marras. Neural and biomechanical tradeoffs associated with human-exoskeleton interactions. Applied Ergonomics. 2021; 96 ():103494.
Chicago/Turabian StyleYibo Zhu; Eric B. Weston; Ranjana K. Mehta; William S. Marras. 2021. "Neural and biomechanical tradeoffs associated with human-exoskeleton interactions." Applied Ergonomics 96, no. : 103494.
Firefighters often need to digest complex spatial information within a short period of time for search and rescue. Previous wayfinding literature has documented evidence about how the general population in normal situations leverage different forms of spatial information, including landmarks, routes and survey (maps), to develop spatial knowledge and guide wayfinding. However, little is known about how the arbitrarily given spatial information affects firefighter wayfinding behavior and performance when the time is limited and there is no privilege for them to develop complete spatial knowledge in an evolving manner. To narrow the knowledge gap, we conducted a wayfinding experiment with firefighters (n = 40) using Virtual Reality (VR). In the experiment, firefighters were required to find three victims in a simulated office maze. Each firefighter randomly experienced four experimental conditions in this study including control condition, landmark condition, route condition, and survey (map) condition. For each experimental condition, firefighters had 3 min to memorize the different layouts of the virtual office mazes using one of three spatial information including landmarks, routes, and survey (map), and then went to the virtual office maze to find the victims. The number of victims found, time, and navigation patterns were used to evaluate firefighters’ wayfinding performance. The results indicated that the route and survey spatial information was more efficient in facilitating firefighter to memorize the layout, leading to a better wayfinding performance. We also found that although the survey information provided more complete information about the layout, it also burdened firefighters in a way that they had to plan the path with limited time. Since survey information may induce different path planning strategies, survey information did not result in a better performance than the route information as suggested by previous studies. This research helps explain the relationship between different forms of spatial information and the wayfinding performance of firefighters. The findings will help professionals design better training protocols and technologies for firefighters.
Yangming Shi; John Kang; Pengxiang Xia; Oshin Tyagi; Ranjana K. Mehta; Jing Du. Spatial knowledge and firefighters’ wayfinding performance: A virtual reality search and rescue experiment. Safety Science 2021, 139, 105231 .
AMA StyleYangming Shi, John Kang, Pengxiang Xia, Oshin Tyagi, Ranjana K. Mehta, Jing Du. Spatial knowledge and firefighters’ wayfinding performance: A virtual reality search and rescue experiment. Safety Science. 2021; 139 ():105231.
Chicago/Turabian StyleYangming Shi; John Kang; Pengxiang Xia; Oshin Tyagi; Ranjana K. Mehta; Jing Du. 2021. "Spatial knowledge and firefighters’ wayfinding performance: A virtual reality search and rescue experiment." Safety Science 139, no. : 105231.
Advancements in robot technology are allowing for increasing integration of humans and robots in shared space manufacturing processes. While individual task performance of the robotic assistance and human operator can be separately optimized, the interaction between humans and robots can lead to emergent effects on collaborative performance. Thus, the performance benefits of increased automation in robotic assistance and its impact by human factors need to be considered. As such, this letter examines the interplay of operator sex, their cognitive fatigue states, and varying levels of automation on collaborative task performance, operator situation awareness and perceived workload, and physiological responses (heart rate variability; HRV). Sixteen participants, balanced by sex, performed metal polishing tasks directly with a UR10 collaborative robot under different fatigued states and with varying levels of robotic assistance. Perceived fatigue, situation awareness, and workload were measured periodically, in addition to continuous physiological monitoring and three task performance metrics: task efficiency, accuracy, and precision, were obtained. Higher robotic assistance demonstrated direct task performance benefits. However, unlike females, males did not perceive the performance benefits as better with higher automation. A relationship between situation awareness and automation was observed in both the HRV signals and subjective measures, where increased robot assistance reduced the attentional supply and task engagement of participants. The consideration of the interplay between human factors, such as operator sex and their cognitive states, and robot factors on collaborative performance can lead to improved human-robot collaborative system designs.
S.K. Hopko; Riya Khurana; Ranjana K. Mehta; Prabhakar R. Pagilla. Effect of Cognitive Fatigue, Operator Sex, and Robot Assistance on Task Performance Metrics, Workload, and Situation Awareness in Human-Robot Collaboration. IEEE Robotics and Automation Letters 2021, 6, 3049 -3056.
AMA StyleS.K. Hopko, Riya Khurana, Ranjana K. Mehta, Prabhakar R. Pagilla. Effect of Cognitive Fatigue, Operator Sex, and Robot Assistance on Task Performance Metrics, Workload, and Situation Awareness in Human-Robot Collaboration. IEEE Robotics and Automation Letters. 2021; 6 (2):3049-3056.
Chicago/Turabian StyleS.K. Hopko; Riya Khurana; Ranjana K. Mehta; Prabhakar R. Pagilla. 2021. "Effect of Cognitive Fatigue, Operator Sex, and Robot Assistance on Task Performance Metrics, Workload, and Situation Awareness in Human-Robot Collaboration." IEEE Robotics and Automation Letters 6, no. 2: 3049-3056.
Fatigue is defined as “a loss of force-generating capacity” in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant’s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier’s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.
Lilia Aljihmani; Oussama Kerdjidj; Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors 2020, 20, 6897 .
AMA StyleLilia Aljihmani, Oussama Kerdjidj, Yibo Zhu, Ranjana K. Mehta, Madhav Erraguntla, Farzan Sasangohar, Khalid Qaraqe. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors. 2020; 20 (23):6897.
Chicago/Turabian StyleLilia Aljihmani; Oussama Kerdjidj; Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. 2020. "Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor." Sensors 20, no. 23: 6897.
Type 1 diabetes (T1D) is associated with reduced muscular strength and greater muscle fatigability. Along with changes in muscular mechanisms, T1D is also linked to structural changes in the brain. How the neurophysiological mechanisms underlying muscle fatigue is altered with T1D and sex related differences of these mechanisms are still not well investigated. The aim of this study was to determine the impact of T1D on the neural correlates of handgrip fatigue and examine sex and T1D related differences in neuromuscular performance parameters, neural activation and functional connectivity patterns between the motor regions of the brain. Forty-two adults, balanced by condition (healthy vs T1D) and sex (male vs female), and performed submaximal isometric handgrip contractions until voluntary exhaustion. Initial strength, endurance time, strength loss, force variability, and complexity measures were collected. Additionally, hemodynamic responses from motor-function related cortical regions, using functional near-infrared spectroscopy (fNIRS), were obtained. Overall, females exhibited lower initial strength (p < 0.0001), and greater strength loss (p = 0.023) than males. While initial strength was significantly lower in the T1D group (p = 0.012) compared to the healthy group, endurance times and strength loss were comparable between the two groups. Force complexity, measured as approximate entropy, was found to be lower throughout the experiment for the T1D group (p = 0.0378), indicating lower online motor adaptability. Although, T1D and healthy groups fatigued similarly, only the T1D group exhibited increased neural activation in the left (p = 0.095) and right (p = 0.072) supplementary motor areas (SMA) over time. A sex × condition × fatigue interaction effect (p = 0.044) showed that while increased activation was observed in both T1D females and healthy males from the Early to Middle phase, this was not observed in healthy females or T1D males. These findings demonstrate that T1D adults had lower adaptability to fatigue which they compensated for by increasing neural effort. This study highlights the importance of examining both neural and motor performance signatures when investigating the impact of chronic conditions on neuromuscular fatigue. Additionally, the findings have implications for developing intervention strategies for training, rehabilitation, and ergonomics considerations for individuals with chronic conditions.
Oshin Tyagi; Yibo Zhu; Connor Johnson; Ranjana K. Mehta; Farzan Sasangohar; Madhav Erraguntla; Khalid Qaraqe. Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women. Frontiers in Human Neuroscience 2020, 14, 1 .
AMA StyleOshin Tyagi, Yibo Zhu, Connor Johnson, Ranjana K. Mehta, Farzan Sasangohar, Madhav Erraguntla, Khalid Qaraqe. Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women. Frontiers in Human Neuroscience. 2020; 14 ():1.
Chicago/Turabian StyleOshin Tyagi; Yibo Zhu; Connor Johnson; Ranjana K. Mehta; Farzan Sasangohar; Madhav Erraguntla; Khalid Qaraqe. 2020. "Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women." Frontiers in Human Neuroscience 14, no. : 1.
Shutdown maintenance, i.e., turning off a facility for a short period for renewal or replacement operations is a highly stressful task. With the limited time and complex operation procedures, human stress is a leading risk. Especially shutdown maintenance workers often need to go through excessive and stressful on-site trainings to digest complex operation information in limited time. The challenge is that workers’ stress status and task performance are hard to predict, as most trainings are only assessed after the shutdown maintenance operation is finished. A proactive assessment or intervention is needed to evaluate workers’ stress status and task performance during the training to enable early warning and interventions. This study proposes a neurophysiological approach to assess workers’ stress status and task performance under different virtual training scenarios. A Virtual Reality (VR) system integrated with the eye-tracking function was developed to simulate the power plant shutdown maintenance operations of replacing a heat exchanger in both normal and stressful scenarios. Meanwhile, a portable neuroimaging device – Functional Near-Infrared Spectroscopy (fNIRS) was also utilized to collect user’s brain activities by measuring hemodynamic responses associated with neuron behavior. A human–subject experiment (n = 16) was conducted to evaluate participants’ neural activity patterns and physiological metrics (gaze movement) related to their stress status and final task performance. Each participant was required to review the operational instructions for a pipe maintenance task for a short period and then perform the task based on their memory in both normal and stressful scenarios. Our experiment results indicated that stressful training had a strong impact on participants’ neural connectivity patterns and final performance, suggesting the use of stressors during training to be an important and useful control factors. We further found significant correlations between gaze movement patterns in review phase and final task performance, and between the neural features and final task performance. In summary, we proposed a variety of supervised machine learning classification models that use the fNIRS data in the review session to estimate individual’s task performance. The classification models were validated with the k-fold (k = 10) cross-validation method. The Random Forest classification model achieved the best average classification accuracy (80.38%) in classifying participants’ task performance compared to other classification models. The contribution of our study is to help establish the knowledge and methodological basis for an early warning and estimating system of the final task performance based on the neurophysiological measures during the training for industrial operations. These findings are expected to provide more evidence about an early performance warning and prediction system based on a hybrid neurophysiological measure method, inspiring the design of a cognition-driven personalized training system for industrial workers.
Yangming Shi; Yibo Zhu; Ranjana K. Mehta; Jing Du. A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS). Advanced Engineering Informatics 2020, 46, 101153 .
AMA StyleYangming Shi, Yibo Zhu, Ranjana K. Mehta, Jing Du. A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS). Advanced Engineering Informatics. 2020; 46 ():101153.
Chicago/Turabian StyleYangming Shi; Yibo Zhu; Ranjana K. Mehta; Jing Du. 2020. "A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS)." Advanced Engineering Informatics 46, no. : 101153.
Background Postflight orthostatic challenge (OC), resulting from blood pooling in lower extremities, is a major health concern among astronauts that fly long-duration missions. Additionally, astronauts undergo physical demanding tasks resulting in acute fatigue, which can affect performance. However, the effects of concurrent OC and acute physical fatigue on performance have not been adequately investigated. Objective The purpose of this study was to determine the relationship between acute physical fatigue and cognitive function during OC. Methods Sixteen healthy participants performed the mental arithmetic task and psychomotor tracking tasks in the absence and presence of a prior 1-hour physically fatiguing exercise, on separate days under OC (induced via lower body negative pressure). We recorded task performances on the cognitive tests and prefrontal cortex oxygenation using functional near-infrared spectroscopy, along with physiological and subjective responses. Results The introduction of the cognitive tasks during OC increased cerebral oxygenation; however, oxygenation decreased significantly with the cognitive tasks under the acute fatigue conditions, particularly during the tracking task and in males. These differences were accompanied by comparable task performances. Discussion The findings suggest that mental arithmetic is a more effective countermeasure than psychomotor tracking under acute physical fatigue during OC. Whereas females did not show a significant difference in cerebral oxygenation due to task, males did, suggesting that it may be important to consider gender differences when developing countermeasures against OC.
Ranjana K. Mehta; Joseph Nuamah. Relationship Between Acute Physical Fatigue and Cognitive Function During Orthostatic Challenge in Men and Women: A Neuroergonomics Investigation. Human Factors: The Journal of the Human Factors and Ergonomics Society 2020, 1 .
AMA StyleRanjana K. Mehta, Joseph Nuamah. Relationship Between Acute Physical Fatigue and Cognitive Function During Orthostatic Challenge in Men and Women: A Neuroergonomics Investigation. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2020; ():1.
Chicago/Turabian StyleRanjana K. Mehta; Joseph Nuamah. 2020. "Relationship Between Acute Physical Fatigue and Cognitive Function During Orthostatic Challenge in Men and Women: A Neuroergonomics Investigation." Human Factors: The Journal of the Human Factors and Ergonomics Society , no. : 1.
Objective We aimed to identify opportunities for application of human factors knowledge base to mitigate disaster management (DM) challenges associated with the unique characteristics of the COVID-19 pandemic. Background The role of DM is to minimize and prevent further spread of the contagion over an extended period of time. This requires addressing large-scale logistics, coordination, and specialized training needs. However, DM-related challenges during the pandemic response and recovery are significantly different than with other kinds of disasters. Method An expert review was conducted to document issues relevant to human factors and ergonomics (HFE) in DM. Results The response to the COVID-19 crisis has presented complex and unique challenges to DM and public health practitioners. Compared to other disasters and previous pandemics, the COVID-19 outbreak has had an unprecedented scale, magnitude, and propagation rate. The high technical complexity of response and DM coupled with lack of mental model and expertise to respond to such a unique disaster has seriously challenged the response work systems. Recent research has investigated the role of HFE in modeling DM systems’ characteristics to improve resilience, accelerating emergency management expertise, developing agile training methods to facilitate dynamically changing response, improving communication and coordination among system elements, mitigating occupational hazards including guidelines for the design of personal protective equipment, and improving procedures to enhance efficiency and effectiveness of response efforts. Conclusion This short review highlights the potential for the field’s contribution to proactive and resilient DM for the ongoing and future pandemics.
Farzan Sasangohar; Jason Moats; Ranjana Mehta; S. Camille Peres. Disaster Ergonomics: Human Factors in COVID-19 Pandemic Emergency Management. Human Factors: The Journal of the Human Factors and Ergonomics Society 2020, 62, 1061 -1068.
AMA StyleFarzan Sasangohar, Jason Moats, Ranjana Mehta, S. Camille Peres. Disaster Ergonomics: Human Factors in COVID-19 Pandemic Emergency Management. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2020; 62 (7):1061-1068.
Chicago/Turabian StyleFarzan Sasangohar; Jason Moats; Ranjana Mehta; S. Camille Peres. 2020. "Disaster Ergonomics: Human Factors in COVID-19 Pandemic Emergency Management." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 7: 1061-1068.
OCCUPATIONAL ABSTRACT There has been increasing use of small unmanned aerial systems in disaster and incident response. We evaluated sUAS pilot states during the tactical response to the 2018 Kilauea Volcano Lower East Rift Zone event, using a 3-minute psychomotor vigilance test (PVT) and wrist worn heart rate sensor. The field data, collected over four days, indicated that the sUAS pilots did not recover to baseline vigilance and physiological levels. Some pilots stopped participating over time, owing to logistical constraints of performing the 3-minute PVT test. Additionally, all pilots refrained from rating their perceived workload levels. We summarize the utility of and challenges associated with collecting performance, physiological, and subjective measures of pilot fatigue during real disaster response.
Ranjana K Mehta; Joseph Nuamah; S. Camille Peres; Robin R. Murphy. Field Methods to Quantify Emergency Responder Fatigue: Lessons Learned from sUAS Deployment at the 2018 Kilauea Volcano Eruption. IISE Transactions on Occupational Ergonomics and Human Factors 2020, 8, 166 -174.
AMA StyleRanjana K Mehta, Joseph Nuamah, S. Camille Peres, Robin R. Murphy. Field Methods to Quantify Emergency Responder Fatigue: Lessons Learned from sUAS Deployment at the 2018 Kilauea Volcano Eruption. IISE Transactions on Occupational Ergonomics and Human Factors. 2020; 8 (3):166-174.
Chicago/Turabian StyleRanjana K Mehta; Joseph Nuamah; S. Camille Peres; Robin R. Murphy. 2020. "Field Methods to Quantify Emergency Responder Fatigue: Lessons Learned from sUAS Deployment at the 2018 Kilauea Volcano Eruption." IISE Transactions on Occupational Ergonomics and Human Factors 8, no. 3: 166-174.
Background Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. Objective This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. Methods A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. Results Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. Conclusions Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.
Elaine Lum; Yu Kuei Lin; Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study. JMIR Diabetes 2020, 5, e17890 .
AMA StyleElaine Lum, Yu Kuei Lin, Karim Zahed, Farzan Sasangohar, Ranjana Mehta, Madhav Erraguntla, Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study. JMIR Diabetes. 2020; 5 (2):e17890.
Chicago/Turabian StyleElaine Lum; Yu Kuei Lin; Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. 2020. "Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study." JMIR Diabetes 5, no. 2: e17890.
Background Advances in technology engender the investigation of technological solutions to opioid use disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited. Objective The overarching aim of our research was to design OUD management technologies that utilize wearable sensors to provide continuous monitoring capabilities. The objectives of this study were to (1) document the currently available opioid-related mHealth apps, (2) review past and existing technology solutions that address OUD, and (3) discuss opportunities for technological withdrawal management solutions. Methods We used a two-phase parallel search approach: (1) an app search to determine the availability of opioid-related mHealth apps and (2) a scoping review of relevant literature to identify relevant technologies and mHealth apps used to address OUD. Results The app search revealed a steady rise in app development, with most apps being clinician-facing. Most of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the scoping review found no study that investigated the efficacy of mHealth apps to address OUD. Conclusions Our findings highlight a general gap in technological solutions of OUD management and the potential for mHealth apps and wearable sensors to address OUD.
Joseph Nuamah; Ranjana Mehta; Farzan Sasangohar. Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review. JMIR mHealth and uHealth 2020, 8, e15752 .
AMA StyleJoseph Nuamah, Ranjana Mehta, Farzan Sasangohar. Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review. JMIR mHealth and uHealth. 2020; 8 (6):e15752.
Chicago/Turabian StyleJoseph Nuamah; Ranjana Mehta; Farzan Sasangohar. 2020. "Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review." JMIR mHealth and uHealth 8, no. 6: e15752.
Neuromuscular fatigue affects workers’ productivity and health, which is further deteriorated with chronic conditions such as type 1 diabetes (T1D). Enhanced physiological tremor, a key indicator of neuromuscular fatigue, shows great potential in detecting the onset of neuromuscular fatigue. This study aims to determine the feasibility of using a cost-effective wearable accelerometer-based microelectromechanical sensor to convey neuromuscular fatigue-related tremor information in healthy and T1D adults. 42 adults (22 healthy, 20 T1D), equipped with a finger and a wrist accelerometer, performed intermittent submaximal isometric handgrip fatigue exercises using a grip dynamometer. Motor variability feature, namely, Coefficient of Variation (CV), and motor complexity feature, namely approximate entropy (ApEn), were extracted from the force signal of dynamometer and from the finger and wrist tremor accelerometry signals and subjected to statistical analysis. First, significant positive correlations were found between tremor accelerometry and force signal in terms of motor variability and complexity features. Second, a three-way (fatigue phase: early, middle, late; gender: male, female; condition: healthy, T1D) analysis of variance resulted in a significant fatigue effect on both accelerometry and force measurements in terms of motor variability and complexity features. Apart from finger CV, no other features showed any gender or condition effects. These findings indicate that finger and wrist tremors measured by accelerometer-based sensors can retain the robustness of fatiguerelated motor variability and complexity. Wrist tremor features were found to capture fatigue development across both healthy and diabetic males and females, thereby offering comparable fatigue detection and management in adults with chronic conditions.
Yibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults. IEEE Sensors Journal 2020, 20, 11183 -11190.
AMA StyleYibo Zhu, Ranjana K. Mehta, Madhav Erraguntla, Farzan Sasangohar, Khalid Qaraqe. Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults. IEEE Sensors Journal. 2020; 20 (19):11183-11190.
Chicago/Turabian StyleYibo Zhu; Ranjana K. Mehta; Madhav Erraguntla; Farzan Sasangohar; Khalid Qaraqe. 2020. "Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults." IEEE Sensors Journal 20, no. 19: 11183-11190.
There are about 44 million licensed older drivers in the U.S. Older adults have higher crash rates and fatalities as compared to middle-aged and young drivers, which might be associated with degradations in sensory, cognitive, and physical capabilities. Advanced driver-assistance systems (ADAS) have the potential to substantially improve safety by removing some of driver vehicle control responsibilities. However, a critical aspect of providing ADAS is educating drivers on their operational characteristics and continued use. Twenty older adults participated in a driving simulation study assessing the effectiveness of video-based and demonstration-based training protocols in learning ADAS considering gender differences. The findings revealed video-based training to be more effective than demonstration-based training in improving driver performance and reducing off-road visual attention allocation and mental workload. In addition, female drivers required lower investment of mental effort (higher neural efficiency) to maintain the performance relative to males and they were less distracted by ADAS. However, male drivers were faster in activating ADAS as compared to females since they were monitoring the status of ADAS features more frequently while driving. The findings of this study provided an empirical support for using video-based approach for learning ADAS in older adults to improve driver safety and supported previous findings on older adults' learning that as age increases there is a tendency to prefer more passive and observational learning methods.
Maryam Zahabi; Ashiq Mohammed Abdul Razak; Ashley E. Shortz; Ranjana K. Mehta; Michael Manser. Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures. Applied Ergonomics 2020, 84, 103036 .
AMA StyleMaryam Zahabi, Ashiq Mohammed Abdul Razak, Ashley E. Shortz, Ranjana K. Mehta, Michael Manser. Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures. Applied Ergonomics. 2020; 84 ():103036.
Chicago/Turabian StyleMaryam Zahabi; Ashiq Mohammed Abdul Razak; Ashley E. Shortz; Ranjana K. Mehta; Michael Manser. 2020. "Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures." Applied Ergonomics 84, no. : 103036.
Information visualization affords us the ability to reason, understand, and gain insight into data. Traditional behavioral and subjective methods of evaluating visualizations are inadequate. Neuroergonomic techniques can be used to complement traditional evaluation methods by assessing noninvasively and unobtrusively participants’ working memory as they engage with the information visualization. In this chapter, we discuss information visualization techniques with an emphasis on evaluating visualizations from a cognitive load perspective. In particular, we review noninvasive functional brain monitoring techniques—electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)—and how they have been used in information visualization research. Both measures of neural activity and task-related performance measures can be combined in a manner that enables the relationship between neural activity and performance to be quantified in terms of efficiency. In this chapter, we discuss visual efficiency, a neuroergonomic metric of information visualization. Finally, we review neuroadaptive interfaces—interfaces that modify their functionality in response to changes in the operator’s cognitive state and needs.
Joseph K. Nuamah; Ranjana K. Mehta. Neuroergonomic Applications in Information Visualization. Computational and Cognitive Neuroscience of Vision 2020, 435 -449.
AMA StyleJoseph K. Nuamah, Ranjana K. Mehta. Neuroergonomic Applications in Information Visualization. Computational and Cognitive Neuroscience of Vision. 2020; ():435-449.
Chicago/Turabian StyleJoseph K. Nuamah; Ranjana K. Mehta. 2020. "Neuroergonomic Applications in Information Visualization." Computational and Cognitive Neuroscience of Vision , no. : 435-449.
Major depressive disorder (MDD) has shown to negatively impact physical recovery in a variety of medical events (e.g., stroke and spinal cord injuries). Yet depression assessments, which are typically subjective in nature, are seldom considered to develop or guide rehabilitation strategies. The present study developed a predictive depression assessment technique using functional near-infrared spectroscopy (fNIRS) that can be rapidly integrated or performed concurrently with existing physical rehabilitation tasks. Thirty-one volunteers, including 14 adults clinically diagnosed with MDD and 17 healthy adults, participated in the study. Brain oxyhemodynamic (HbO) responses were recorded using a 16-channel wearable continuous-wave fNIRS device while the volunteers performed the Grasp and Release Test in four 16-minute blocks. Ten features, extracted from HbO signals, from each channel served as inputs to XGBoost and Random Forest algorithms developed for each block and combination of successive blocks. Top 5 common features resulted in a classification accuracy of 92.6%, sensitivity of 84.8%, and specificity of 91.7% using the XGBoost classifier. This study identified mean HbO, full width half maximum and kurtosis, as specific neuromarkers, for predicting MDD across specific depression-related regions of interests (i.e., dorsolateral and ventrolateral prefrontal cortex). Our results suggest that a wearable fNIRS head probe monitoring specific brain regions, and limiting extraction to few features, can enable quick setup and rapid assessment of depression in patients. The overarching goal is to embed predictive neurotechnology during post-stroke and post-spinalcord-injury rehabilitation sessions to monitor patients' depression symptomology so as to actively guide decisions about motor therapies.
Yibo Zhu; Jagadish K. Jayagopal; Ranjana K. Mehta; Madhav Erraguntla; Joseph Nuamah; Anthony D. McDonald; Heather Taylor; Shuo-Hsiu Chang. Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 961 -969.
AMA StyleYibo Zhu, Jagadish K. Jayagopal, Ranjana K. Mehta, Madhav Erraguntla, Joseph Nuamah, Anthony D. McDonald, Heather Taylor, Shuo-Hsiu Chang. Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (4):961-969.
Chicago/Turabian StyleYibo Zhu; Jagadish K. Jayagopal; Ranjana K. Mehta; Madhav Erraguntla; Joseph Nuamah; Anthony D. McDonald; Heather Taylor; Shuo-Hsiu Chang. 2020. "Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 4: 961-969.
BACKGROUND Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. OBJECTIVE This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. METHODS A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. RESULTS Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. CONCLUSIONS Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.
Karim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint). 2020, 1 .
AMA StyleKarim Zahed, Farzan Sasangohar, Ranjana Mehta, Madhav Erraguntla, Khalid Qaraqe. Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint). . 2020; ():1.
Chicago/Turabian StyleKarim Zahed; Farzan Sasangohar; Ranjana Mehta; Madhav Erraguntla; Khalid Qaraqe. 2020. "Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint)." , no. : 1.
Sensory feedback, which can be presented in different modalities – single and combined, aids task performance in human–robotic interaction (HRI). However, combining feedback modalities does not always lead to optimal performance. Indeed, it is not known how feedback modalities affect operator performance under stress. Furthermore, there is limited information on how feedback affects neural processes differently for males and females and under stress. This is a critical gap in the literature, particularly in the domain of surgical robotics, where surgeons are under challenging socio-technical environments that burden them physiologically. In the present study, we posited operator performance as the summation of task performance and neurophysiological cost of maintaining that performance. In a within-subject design, we used functional near-infrared spectroscopy to assess cerebral activations of 12 participants who underwent a 3D manipulation task within a virtual environment with concurrent feedback (visual and visual + haptic) in the presence and absence of a cognitive stressor. Cognitive stress was induced with the serial-7 subtraction test. We found that while task performance was higher with visual than visual + haptic feedback, it degraded under stress. The two feedback modalities were found to be associated with varying neural activities and neural efficiencies, and these were stress- and gender-dependent. Our findings engender further investigation into effectiveness of feedback modalities on males and females under stressful conditions in HRI.
Joseph K. Nuamah; Whitney Mantooth; Rohith Karthikeyan; Ranjana K. Mehta; Seok Chang Ryu. Neural Efficiency of Human–Robotic Feedback Modalities Under Stress Differs With Gender. Frontiers in Human Neuroscience 2019, 13, 287 .
AMA StyleJoseph K. Nuamah, Whitney Mantooth, Rohith Karthikeyan, Ranjana K. Mehta, Seok Chang Ryu. Neural Efficiency of Human–Robotic Feedback Modalities Under Stress Differs With Gender. Frontiers in Human Neuroscience. 2019; 13 ():287.
Chicago/Turabian StyleJoseph K. Nuamah; Whitney Mantooth; Rohith Karthikeyan; Ranjana K. Mehta; Seok Chang Ryu. 2019. "Neural Efficiency of Human–Robotic Feedback Modalities Under Stress Differs With Gender." Frontiers in Human Neuroscience 13, no. : 287.
The objective use of table top adjustable sit-stand desks has yet to be determined, due to the lack of an effective digital evaluation method. The objective of this study was to evaluate the impact of computer prompt software on table top sit-stand desks to determine if there was a difference in the frequency of desk position changes. This five month, pre-post pilot study on 47 university staff members used a novel USB accelerometer sensor and computer software reminders to continuously record and prompt increases in desk usage to promote physical activity at the workstation. During the baseline phase (3 months), desk usage data were continuously recorded for all workers. Following the baseline, the results from a two-month intervention of personalized computer reminders doubled the number of desk position changes per work day from 1 desk position change every 2 work days to 1 change every work day. Furthermore, those who changed desk positions once or twice a day increased from 4% to 36% from baseline to intervention. Overall, the intervention was encouraging, but longer intervention studies are warranted to determine if the desk usage behavior change can be improved and sustained for years and whether that change results in health gains.
Pankaj Sharma; Adam Pickens; Ranjana Mehta; Gang Han; Mark E. Benden. Smart Software Can Increase Sit-Stand Desk Transitions During Active Computer Use. International Journal of Environmental Research and Public Health 2019, 16, 2438 .
AMA StylePankaj Sharma, Adam Pickens, Ranjana Mehta, Gang Han, Mark E. Benden. Smart Software Can Increase Sit-Stand Desk Transitions During Active Computer Use. International Journal of Environmental Research and Public Health. 2019; 16 (13):2438.
Chicago/Turabian StylePankaj Sharma; Adam Pickens; Ranjana Mehta; Gang Han; Mark E. Benden. 2019. "Smart Software Can Increase Sit-Stand Desk Transitions During Active Computer Use." International Journal of Environmental Research and Public Health 16, no. 13: 2438.