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For the first time, this paper reports a smart museum archive box that features a fully integrated wireless powered temperature and humidity sensor. The smart archive box has been specifically developed for microclimate environmental monitoring of stored museum artifacts in cultural heritage applications. The developed sensor does not require a battery and is wirelessly powered using Near Field Communications (NFC). The proposed solution enables a convenient means for wireless sensing with the operator by simply placing a standard smartphone in close proximity to the cardboard archive box. Wireless sensing capability has the advantage of enabling long-term environmental monitoring of the contents of the archive box without having to move and open the box for reading or battery replacement. This contributes to a sustainable preventive conservation strategy and avoids the risk of exposing the contents to the external environment, which may result in degradation of the stored artifacts. In this work, a low-cost and fully integrated NFC sensor has been successfully developed and demonstrated. The developed sensor is capable of wirelessly measuring temperature and relative humidity with a mean error of 0.37 °C and ±0.35%, respectively. The design has also been optimized for low power operation with a measured peak DC power consumption of 900 μW while yielding a 4.5 cm wireless communication range. The power consumption of the NFC sensor is one of the lowest found in the literature. To the author’s knowledge, the NFC sensor proposed in this paper is the first reporting of a smart archive box that is wirelessly powered and uniquely integrated within a cardboard archive box.
Dinesh Gawade; Steffen Ziemann; Sanjeev Kumar; Daniela Iacopino; Marco Belcastro; Davide Alfieri; Katharina Schuhmann; Manfred Anders; Melusine Pigeon; John Barton; Brendan O’Flynn; John Buckley. A Smart Archive Box for Museum Artifact Monitoring Using Battery-Less Temperature and Humidity Sensing. Sensors 2021, 21, 4903 .
AMA StyleDinesh Gawade, Steffen Ziemann, Sanjeev Kumar, Daniela Iacopino, Marco Belcastro, Davide Alfieri, Katharina Schuhmann, Manfred Anders, Melusine Pigeon, John Barton, Brendan O’Flynn, John Buckley. A Smart Archive Box for Museum Artifact Monitoring Using Battery-Less Temperature and Humidity Sensing. Sensors. 2021; 21 (14):4903.
Chicago/Turabian StyleDinesh Gawade; Steffen Ziemann; Sanjeev Kumar; Daniela Iacopino; Marco Belcastro; Davide Alfieri; Katharina Schuhmann; Manfred Anders; Melusine Pigeon; John Barton; Brendan O’Flynn; John Buckley. 2021. "A Smart Archive Box for Museum Artifact Monitoring Using Battery-Less Temperature and Humidity Sensing." Sensors 21, no. 14: 4903.
Aquaculture farming faces challenges to increase production while maintaining welfare of livestock, efficiently use of resources, and being environmentally sustainable. To help overcome these challenges, remote and real-time monitoring of the environmental and biological conditions of the aquaculture site is highly important. Multiple remote monitoring solutions for investigating the growth of seaweed are available, but no integrated solution that monitors different biotic and abiotic factors exists. A new integrated multi-sensing system would reduce the cost and time required to deploy the system and provide useful information on the dynamic forces affecting the plants and the associated biomass of the harvest. In this work, we present the development of a novel miniature low-power NFC-enabled data acquisition system to monitor seaweed growth parameters in an aquaculture context. It logs temperature, light intensity, depth, and motion, and these data can be transmitted or downloaded to enable informed decision making for the seaweed farmers. The device is fully customisable and designed to be attached to seaweed or associated mooring lines. The developed system was characterised in laboratory settings to validate and calibrate the embedded sensors. It performs comparably to commercial environmental sensors, enabling the use of the device to be deployed in commercial and research settings.
Caroline Peres; Masoud Emam; Hamed Jafarzadeh; Marco Belcastro; Brendan O’Flynn. Development of a Low-Power Underwater NFC-Enabled Sensor Device for Seaweed Monitoring. Sensors 2021, 21, 4649 .
AMA StyleCaroline Peres, Masoud Emam, Hamed Jafarzadeh, Marco Belcastro, Brendan O’Flynn. Development of a Low-Power Underwater NFC-Enabled Sensor Device for Seaweed Monitoring. Sensors. 2021; 21 (14):4649.
Chicago/Turabian StyleCaroline Peres; Masoud Emam; Hamed Jafarzadeh; Marco Belcastro; Brendan O’Flynn. 2021. "Development of a Low-Power Underwater NFC-Enabled Sensor Device for Seaweed Monitoring." Sensors 21, no. 14: 4649.
Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community’s interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system’s high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice.
Salvatore Tedesco; Davide Alfieri; Eduardo Perez-Valero; Dimitrios-Sokratis Komaris; Luke Jordan; Marco Belcastro; John Barton; Liam Hennessy; Brendan O’Flynn. A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks. Applied Sciences 2021, 11, 5258 .
AMA StyleSalvatore Tedesco, Davide Alfieri, Eduardo Perez-Valero, Dimitrios-Sokratis Komaris, Luke Jordan, Marco Belcastro, John Barton, Liam Hennessy, Brendan O’Flynn. A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks. Applied Sciences. 2021; 11 (11):5258.
Chicago/Turabian StyleSalvatore Tedesco; Davide Alfieri; Eduardo Perez-Valero; Dimitrios-Sokratis Komaris; Luke Jordan; Marco Belcastro; John Barton; Liam Hennessy; Brendan O’Flynn. 2021. "A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks." Applied Sciences 11, no. 11: 5258.
Lower-limbs kinematics and muscle electrical activity are typically adopted as feedback during rehabilitation sessions or athletes training to provide patients’ progress evaluation or athletic performance information. However, the complexity of motion tracking and surface electromyography (sEMG) systems limits the use of such technologies to laboratory settings and requires special training and expertise to carry out accurate measurements. This paper presents a new wearable textile-based muscle activity and motion sensing device for human lower-limbs, which is capable of recording and wirelessly transmitting sEMG data for several specific muscles as well as kinematic parameters, allowing outdoor and at-home use without direct supervision by non-expert users. In particular, this work is focused on the development and analysis of textile electrodes and garment design, as well as the definition of a proof-of-concept study for sEMG data recording. Obtained values were compared against average rectified values (ARV) recorded using a gold-standard conventional wireless sEMG system. Apart from one muscle (vastus medialis), the developed device showed overall promising results in the muscle activity sensing for lower-limbs, highlighting its possible use in the rehabilitation and sport performance fields. In addition, a washing test was conducted on the electrodes, where it was shown that the proposed textile electrodes maintained structural integrity and showed an acceptable level of electrical parameters deterioration when comparing pre and post washing characteristics.
Liudmila Khokhlova; Marco Belcastro; Pasqualino Torchia; Brendan O’Flynn; Salvatore Tedesco. Wearable Textile-Based Device for Human Lower-Limbs Kinematics and Muscle Activity Sensing. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021, 70 -81.
AMA StyleLiudmila Khokhlova, Marco Belcastro, Pasqualino Torchia, Brendan O’Flynn, Salvatore Tedesco. Wearable Textile-Based Device for Human Lower-Limbs Kinematics and Muscle Activity Sensing. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2021; ():70-81.
Chicago/Turabian StyleLiudmila Khokhlova; Marco Belcastro; Pasqualino Torchia; Brendan O’Flynn; Salvatore Tedesco. 2021. "Wearable Textile-Based Device for Human Lower-Limbs Kinematics and Muscle Activity Sensing." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 70-81.
The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.
Matteo Menolotto; Dimitrios-Sokratis Komaris; Salvatore Tedesco; Brendan O’Flynn; Michael Walsh. Motion Capture Technology in Industrial Applications: A Systematic Review. Sensors 2020, 20, 5687 .
AMA StyleMatteo Menolotto, Dimitrios-Sokratis Komaris, Salvatore Tedesco, Brendan O’Flynn, Michael Walsh. Motion Capture Technology in Industrial Applications: A Systematic Review. Sensors. 2020; 20 (19):5687.
Chicago/Turabian StyleMatteo Menolotto; Dimitrios-Sokratis Komaris; Salvatore Tedesco; Brendan O’Flynn; Michael Walsh. 2020. "Motion Capture Technology in Industrial Applications: A Systematic Review." Sensors 20, no. 19: 5687.
The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.
Sebastian Scheurer; Salvatore Tedesco; Brendan O’Flynn; Kenneth N. Brown. Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance. Sensors 2020, 20, 3647 .
AMA StyleSebastian Scheurer, Salvatore Tedesco, Brendan O’Flynn, Kenneth N. Brown. Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance. Sensors. 2020; 20 (13):3647.
Chicago/Turabian StyleSebastian Scheurer; Salvatore Tedesco; Brendan O’Flynn; Kenneth N. Brown. 2020. "Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance." Sensors 20, no. 13: 3647.
Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.
Salvatore Tedesco; Colum Crowe; Andrew Ryan; Marco Sica; Sebastian Scheurer; Amanda M. Clifford; Kenneth N. Brown; Brendan O’Flynn. Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players. Sensors 2020, 20, 3029 .
AMA StyleSalvatore Tedesco, Colum Crowe, Andrew Ryan, Marco Sica, Sebastian Scheurer, Amanda M. Clifford, Kenneth N. Brown, Brendan O’Flynn. Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players. Sensors. 2020; 20 (11):3029.
Chicago/Turabian StyleSalvatore Tedesco; Colum Crowe; Andrew Ryan; Marco Sica; Sebastian Scheurer; Amanda M. Clifford; Kenneth N. Brown; Brendan O’Flynn. 2020. "Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players." Sensors 20, no. 11: 3029.
Since the 1990's, researchers in both academia and industry have been exploring ways to exploit the potential for Wireless Sensor Networks (WSNs) to revolutionize our understanding of - and interaction with - the world around us. WSNs have therefore been a major focus of research over the past 20 years. While WSNs offer a persuasive solution for accurate real-time sensing of the physical world, they are yet to be as ubiquitous as originally predicted when the technology was first envisaged. Technical difficulties exist which have inhibited the anticipated uptake in WSN technologies. The most challenging of these have been identified as system reliability, battery lifetime, maintenance requirements, node size and ease of use. Over the past decade, the Wireless Sensor Networks (WSN) group at the Tyndall National Institute, has been at the forefront of driving the vision of ubiquitously deployed, extended lifetime, low power consumption embedded systems providing information rich data streams wirelessly in (close to) real-time. In this time, the WSN group has developed multiple novel, first of kind, wireless multi-sensor systems and deployed these in the world around us, overcoming the technical challenges associated with ensuring robust and reliable long-term data sets from our environment. This work is focused on investigating and addressing these challenges through the development of the new technologies and system integration methodologies required to facilitate and implement WSNs and validate these in real deployments. Specifically, discussed are the development and deployment of novel WSN systems in the built environment, environmental monitoring and fitness and health monitoring systems.The key research challenges identified and discussed are:a)The development of resource-constrained, extremely low power consumption systems incorporating energy-efficient hardware and software algorithms.b)The development of highly reliable extremely long duration deployments which through the use of appropriate energy harvesting solutions facilitate (near) zero maintenance sensor networks.c)The development of low power consumption miniaturized wearable microsysteThe development of technologies to address these challenges in terms of cost, size, power consumption and reliability which need to be tested and validated in real world deployments of wireless sensing systems is discussed. It is clear that when looking at the scale up of deployments of novel WSNs, that to be successful, such systems need to "be invisible, last forever, cost nothing and work out of the box". This paper describes these relevant technologies and associated project demonstrators
Peter Haigh; Michael Hayes; Dinesh R. Gawade; Brendan O'Flynn. Towards Autonomous Smart Sensing Systems. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) 2020, 1 -6.
AMA StylePeter Haigh, Michael Hayes, Dinesh R. Gawade, Brendan O'Flynn. Towards Autonomous Smart Sensing Systems. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). 2020; ():1-6.
Chicago/Turabian StylePeter Haigh; Michael Hayes; Dinesh R. Gawade; Brendan O'Flynn. 2020. "Towards Autonomous Smart Sensing Systems." 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) , no. : 1-6.
A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO2) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.
Sanjeev Kumar; John L. Buckley; John Barton; Melusine Pigeon; Robert Newberry; Matthew Rodencal; Adhurim Hajzeraj; Tim Hannon; Ken Rogers; Declan Casey; Donal O’Sullivan; Brendan O’Flynn. A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications. Sensors 2020, 20, 1675 .
AMA StyleSanjeev Kumar, John L. Buckley, John Barton, Melusine Pigeon, Robert Newberry, Matthew Rodencal, Adhurim Hajzeraj, Tim Hannon, Ken Rogers, Declan Casey, Donal O’Sullivan, Brendan O’Flynn. A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications. Sensors. 2020; 20 (6):1675.
Chicago/Turabian StyleSanjeev Kumar; John L. Buckley; John Barton; Melusine Pigeon; Robert Newberry; Matthew Rodencal; Adhurim Hajzeraj; Tim Hannon; Ken Rogers; Declan Casey; Donal O’Sullivan; Brendan O’Flynn. 2020. "A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications." Sensors 20, no. 6: 1675.
Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.
Sebastian Scheurer; Salvatore Tedesco; Kenneth N. Brown; Brendan O’Flynn. Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition. Sensors 2020, 20, 1208 .
AMA StyleSebastian Scheurer, Salvatore Tedesco, Kenneth N. Brown, Brendan O’Flynn. Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition. Sensors. 2020; 20 (4):1208.
Chicago/Turabian StyleSebastian Scheurer; Salvatore Tedesco; Kenneth N. Brown; Brendan O’Flynn. 2020. "Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition." Sensors 20, no. 4: 1208.
Dimitrios-Sokratis Komaris; Eduardo Perez-Valero; Luke Jordan; John Barton; Liam Hennessy; Brendan O'flynn; Salvatore Tedesco. Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running. Journal of Biomechanics 2020, 99, 1 .
AMA StyleDimitrios-Sokratis Komaris, Eduardo Perez-Valero, Luke Jordan, John Barton, Liam Hennessy, Brendan O'flynn, Salvatore Tedesco. Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running. Journal of Biomechanics. 2020; 99 ():1.
Chicago/Turabian StyleDimitrios-Sokratis Komaris; Eduardo Perez-Valero; Luke Jordan; John Barton; Liam Hennessy; Brendan O'flynn; Salvatore Tedesco. 2020. "Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running." Journal of Biomechanics 99, no. : 1.
Tyndall National Institute has developed a glovelike device for Human Computer Interaction based on inertial sensors. Industry 4.0 represents one of the main applications for the possibility to control and monitor integrated systems. Current research focuses on enhancing bidirectional latency, sensor modalities, haptic feedback, interoperability, mainly concerning collaborative robotics scenarios.
Brendan O'Flynn; Javier Sanchez-Torres; Salvatore Tedesco; Michael Walsh. Challenges in the Development of Wearable Human Machine Interface Systems. 2019 IEEE International Electron Devices Meeting (IEDM) 2019, 10.4.1 -10.4.4.
AMA StyleBrendan O'Flynn, Javier Sanchez-Torres, Salvatore Tedesco, Michael Walsh. Challenges in the Development of Wearable Human Machine Interface Systems. 2019 IEEE International Electron Devices Meeting (IEDM). 2019; ():10.4.1-10.4.4.
Chicago/Turabian StyleBrendan O'Flynn; Javier Sanchez-Torres; Salvatore Tedesco; Michael Walsh. 2019. "Challenges in the Development of Wearable Human Machine Interface Systems." 2019 IEEE International Electron Devices Meeting (IEDM) , no. : 10.4.1-10.4.4.
In this paper, the challenges associated with the design of a novel multi-sensor wearable system for the objective assessment of exercises during lower-limbs rehabilitation are described. The overall system architecture is defined, and finally both the implemented hardware and software platforms are illustrated in detail. Multiple sensing technologies are adopted including motion data, electromyography measurements, and muscle electro-stimulation. The software stack provides guidance to the users throughout the rehabilitation therapy sessions, and allows clinicians to access the data collected remotely in real-time thus supporting their clinical evaluation. Finally, preliminary results of the comparison between the knee joint angle estimated by the developed system against a gold-standard inertial-based system are provided showing promising results for future validation.
Salvatore Tedesco; Marco Belcastro; Oscar Manzano Torre; Pasqualino Torchia; Davide Alfieri; Liudmila Khokhlova; Brendan O'Flynn. A Multi-Sensors Wearable System for Remote Assessment of Physiotherapy Exercises during ACL Rehabilitation. 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS) 2019, 237 -240.
AMA StyleSalvatore Tedesco, Marco Belcastro, Oscar Manzano Torre, Pasqualino Torchia, Davide Alfieri, Liudmila Khokhlova, Brendan O'Flynn. A Multi-Sensors Wearable System for Remote Assessment of Physiotherapy Exercises during ACL Rehabilitation. 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). 2019; ():237-240.
Chicago/Turabian StyleSalvatore Tedesco; Marco Belcastro; Oscar Manzano Torre; Pasqualino Torchia; Davide Alfieri; Liudmila Khokhlova; Brendan O'Flynn. 2019. "A Multi-Sensors Wearable System for Remote Assessment of Physiotherapy Exercises during ACL Rehabilitation." 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS) , no. : 237-240.
Dimitrios-Sokratis Komaris; Eduardo Perez-Valero; Luke Jordan; John Barton; Liam Hennessy; Brendan O'Flynn; Salvatore Tedesco. Predicting Three-Dimensional Ground Reaction Forces in Running by Using Artificial Neural Networks and Lower Body Kinematics. IEEE Access 2019, 7, 156779 -156786.
AMA StyleDimitrios-Sokratis Komaris, Eduardo Perez-Valero, Luke Jordan, John Barton, Liam Hennessy, Brendan O'Flynn, Salvatore Tedesco. Predicting Three-Dimensional Ground Reaction Forces in Running by Using Artificial Neural Networks and Lower Body Kinematics. IEEE Access. 2019; 7 ():156779-156786.
Chicago/Turabian StyleDimitrios-Sokratis Komaris; Eduardo Perez-Valero; Luke Jordan; John Barton; Liam Hennessy; Brendan O'Flynn; Salvatore Tedesco. 2019. "Predicting Three-Dimensional Ground Reaction Forces in Running by Using Artificial Neural Networks and Lower Body Kinematics." IEEE Access 7, no. : 156779-156786.
Background Wearable technology is a fast developing area. Often, the focus of research is on accuracy, while the practicalities of using the device may be overlooked, despite the fact that this greatly influences utility. This scoping review therefore explored the design and usability preferences of people for wearable technology for health monitoring. Methods A scoping review was conducted of literature evaluating user preferences for the design of wearable technology systems, for people aged >50 years, with good health, or chronic diseases. Results A search of relevant databases yielded 628 potential studies (after duplicates removed). Following title/abstract and then full text screening, 17 papers were included. The most commonly reported theme related to design and user interface (13 studies). Users wanted a small, unobtrusive and light device which doesn’t snag on clothing or affect activities of daily living, but yet has a readable and easy-to-use interface, which may prove challenging for designers! Users were most happy to wear a device on the wrist and/or hip region, being considered the least obtrusive / most discrete. Users were open to the technology aspects of the device, but wanted specific training, or clear and readable instructions. Less commonly reported parameters included issues with privacy and ownership of data (two studies); cost (two studies); reliability and accuracy (three studies), including being accurate overnight and in the shower, etc.; and clinical usefulness, i.e. the data being effectively linked with other healthcare data. Where considered, participants didn’t want to wear a device by night (two studies). Safety of wearable devices was not a theme in any study. Conclusion Overall, user needs seem to be rarely considered in the design of wearable technology for health monitoring. However, the limited studies do highlight important user concerns, which should be considered by the technology designers and prescribers.
Clíona O'Riordan; Lorna Kenny; Salvatore Tedesco; Marco Sica; Colum Crowe; John Barton; Suzanne Timmons; Brendan O'Flynn. 194 User Preferences for the Design of Wearable Technology Systems - A Scoping Review. Age And Ageing 2019, 48, iii17 -iii65.
AMA StyleClíona O'Riordan, Lorna Kenny, Salvatore Tedesco, Marco Sica, Colum Crowe, John Barton, Suzanne Timmons, Brendan O'Flynn. 194 User Preferences for the Design of Wearable Technology Systems - A Scoping Review. Age And Ageing. 2019; 48 (Supplement):iii17-iii65.
Chicago/Turabian StyleClíona O'Riordan; Lorna Kenny; Salvatore Tedesco; Marco Sica; Colum Crowe; John Barton; Suzanne Timmons; Brendan O'Flynn. 2019. "194 User Preferences for the Design of Wearable Technology Systems - A Scoping Review." Age And Ageing 48, no. Supplement: iii17-iii65.
Sebastian Scheurer; Salvatore Tedesco; Kenneth N. Brown; Brendan O'Flynn. Subject-dependent and -independent human activity recognition with person-specific and -independent models. Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction 2019, 1 .
AMA StyleSebastian Scheurer, Salvatore Tedesco, Kenneth N. Brown, Brendan O'Flynn. Subject-dependent and -independent human activity recognition with person-specific and -independent models. Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction. 2019; ():1.
Chicago/Turabian StyleSebastian Scheurer; Salvatore Tedesco; Kenneth N. Brown; Brendan O'Flynn. 2019. "Subject-dependent and -independent human activity recognition with person-specific and -independent models." Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction , no. : 1.
Background Wrist-worn activity trackers have experienced a tremendous growth lately. Robust studies of the comparative accuracy of currently available, mainstream trackers, in young adults versus older adults are still scarce in literature. This study explores the performance of ten trackers estimating steps, travelled distance, and heart-rate measurements against gold-standards in two cohorts of young and old adults. Methods Overall, 38 subjects completed a structured protocol involving walking tasks, simulated household activities, and sedentary activities, including less standardised activities, such as dusting, vacuuming, or playing cards, in order to simulate real-life scenarios. Both wrist-mounted and chest/waist-mounted devices were considered. Gold-standards included treadmill, waist-mounted pedometer, ECG-based chest strap, direct observation or video recording according to the activity and parameter. Results Every tracker shows a decreasing accuracy with slower walking speed, which resulted in a significant step under-counting. Large mean absolute percentage error (MAPE) was displayed by every monitor at slower walking speeds. During household activities, the MAPE in young adults climbing up/down-stairs ranged from 3.91-11.41% and 4.34-11.92% (dominant and non-dominant arm), respectively. However, for the same activities older adults displayed a larger MAPE, at 8.38-19.3% and 10.06-19.01%, respectively. Chest-worn or waist-worn devices had more uniform performance. However, unstructured activities (dusting, vacuuming, playing cards), and accuracy in people using a walking aid represent a challenge for all consumer-level trackers as evidenced by large MAPE. Poor performance in travelled distance estimation was also evident during walking at low speeds and household activities for both cohorts. Conclusion This study shows a number of limitations to current, mainstream consumer-level wrist-based activity trackers, requiring caution if adopted in healthcare, whether clinical or research. This study demonstrates the particular deficits in commercial devices for use in an aging population, and provides some indications on how to best measure these health parameters in this population.
John Barton; Suzanne Timmons; Salvatore Tedesco; Marco Sica; Colum Crowe; Brendan O'Flynn. 95 Accuracy of Off the Shelf Activity Trackers in Ambulatory Settings in Young and Old Adults. Age And Ageing 2019, 48, iii1 -iii16.
AMA StyleJohn Barton, Suzanne Timmons, Salvatore Tedesco, Marco Sica, Colum Crowe, Brendan O'Flynn. 95 Accuracy of Off the Shelf Activity Trackers in Ambulatory Settings in Young and Old Adults. Age And Ageing. 2019; 48 (Supplement):iii1-iii16.
Chicago/Turabian StyleJohn Barton; Suzanne Timmons; Salvatore Tedesco; Marco Sica; Colum Crowe; Brendan O'Flynn. 2019. "95 Accuracy of Off the Shelf Activity Trackers in Ambulatory Settings in Young and Old Adults." Age And Ageing 48, no. Supplement: iii1-iii16.
Transport systems incorporating linear synchronous motors (LSMs) enable linear motion at high speed for emerging factory automation applications. The goal of this work is to determine the feasibility of harvesting energy directly from an operational LSM transport system employed in high volume manufacturing. Microelectromechanical (MEMs) based sensor technology, deployed as part of a wireless cyber physical system (CPS), perform near real-time magnetic field measurement for a mobile LSM vehicle. The vehicle under study is purposed for mobile factory automation and is not wired for communications nor does it have an onboard power source. A series of experiments were designed and conducted to establish the magnetic profile of the system. Empirical data capture was conducted on a cycled LSM test-bed comprising of 2 shuttles and 2 x 3 meter lengths of LSM track (MagneMotion QuickStick®100). Varying vehicle speeds were incorporated in the experimental regime to determine how changes in velocity would impact the magnetic profile of the vehicle. The recorded magnetic field data was analysed and a relationship between LSM vehicle speed and magnetic field frequency was established. The study highlights the potential to employ a single receiving coil to enable energy recovery which in turn could power a cyber-physical system (CPS) tasked with performing condition based monitoring of the LSM transport vehicles. This in turn can form the basis for the development of a predictive maintenance system, deployed to an LSM based transport layer in high volume manufacturing environments.
Michael Walsh; Giovanni Abbruzzo; Seamus Hickey; Sonia Ramirez-Garcia; Brendan O'Flynn; Javier Torres. On the potential for Electromagnetic Energy Harvesting for a Linear Synchronous Motor based Transport System in Factory Automation. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 2019, 864 -869.
AMA StyleMichael Walsh, Giovanni Abbruzzo, Seamus Hickey, Sonia Ramirez-Garcia, Brendan O'Flynn, Javier Torres. On the potential for Electromagnetic Energy Harvesting for a Linear Synchronous Motor based Transport System in Factory Automation. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2019; ():864-869.
Chicago/Turabian StyleMichael Walsh; Giovanni Abbruzzo; Seamus Hickey; Sonia Ramirez-Garcia; Brendan O'Flynn; Javier Torres. 2019. "On the potential for Electromagnetic Energy Harvesting for a Linear Synchronous Motor based Transport System in Factory Automation." 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) , no. : 864-869.
Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%. The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.
Salvatore Tedesco; Marco Sica; Andrea Ancillao; Suzanne Timmons; John Barton; Brendan O'flynn. Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort. JMIR mHealth and uHealth 2019, 7, e13084 .
AMA StyleSalvatore Tedesco, Marco Sica, Andrea Ancillao, Suzanne Timmons, John Barton, Brendan O'flynn. Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort. JMIR mHealth and uHealth. 2019; 7 (6):e13084.
Chicago/Turabian StyleSalvatore Tedesco; Marco Sica; Andrea Ancillao; Suzanne Timmons; John Barton; Brendan O'flynn. 2019. "Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort." JMIR mHealth and uHealth 7, no. 6: e13084.
Wrist-worn activity trackers have experienced a tremendous growth lately and studies on the accuracy of mainstream trackers used by older adults are needed. This study explores the performance of six trackers (Fitbit Charge2, Garmin VivoSmart HR+, Philips Health Watch, Withings Pulse Ox, ActiGraph GT9X-BT, Omron HJ-72OITC) for estimating: steps, travelled distance, and heart-rate measurements for a cohort of older adults. Eighteen older adults completed a structured protocol involving walking tasks, simulated household activities, and sedentary activities. Less standardized activities were also included, such as: dusting, using a walking aid, or playing cards, in order to simulate real-life scenarios. Wrist-mounted and chest/waist-mounted devices were used. Gold-standards included treadmill, ECG-based chest strap, direct observation or video recording according to the activity and parameter. Every tracker showed a decreasing accuracy with slower walking speed, which resulted in a significant step under-counting. A large mean absolute percentage error (MAPE) was found for every monitor at slower walking speeds with the lowest reported MAPE at 2 km/h being 7.78%, increasing to 20.88% at 1.5 km/h, and 44.53% at 1 km/h. During household activities, the MAPE climbing up/down-stairs ranged from 8.38–19.3% and 10.06–19.01% (dominant and non-dominant arm), respectively. Waist-worn devices showed a more uniform performance. However, unstructured activities (e.g. dusting, playing cards), and using a walking aid represent a challenge for all wrist-worn trackers as evidenced by large MAPE (> 57.66% for dusting, > 67.32% when using a walking aid). Poor performance in travelled distance estimation was also evident during walking at low speeds and climbing up/down-stairs (MAPE > 71.44% and > 48.3%, respectively). Regarding heart-rate measurement, there was no significant difference (p-values > 0.05) in accuracy between trackers placed on the dominant or non-dominant arm. Concordant with existing literature, while the mean error was limited (between -3.57 bpm and 4.21 bpm), a single heart-rate measurement could be underestimated up to 30 beats-per-minute. This study showed a number of limitations of consumer-level wrist-based activity trackers for older adults. Therefore caution is required when used, in healthcare or in research settings, to measure activity in older adults.
Salvatore Tedesco; Marco Sica; Andrea Ancillao; Suzanne Timmons; John Barton; Brendan O'Flynn. Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults. PLOS ONE 2019, 14, e0216891 .
AMA StyleSalvatore Tedesco, Marco Sica, Andrea Ancillao, Suzanne Timmons, John Barton, Brendan O'Flynn. Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults. PLOS ONE. 2019; 14 (5):e0216891.
Chicago/Turabian StyleSalvatore Tedesco; Marco Sica; Andrea Ancillao; Suzanne Timmons; John Barton; Brendan O'Flynn. 2019. "Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults." PLOS ONE 14, no. 5: e0216891.