In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human–robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.

Background: Physical activity (PA) is vital for attenuating the aging-related physiological and functional declines in women aged 60 years or above. However, little is known about the objectively assessed PA behavior in older women during the COVID-19 pandemic and its association with sociodemographics, health and physical function, and COVID-19 related factors. Objective: This study aims to examine the objectively measured PA levels and associated factors among older US women who were living under the physical distancing guidelines during the second year of the pandemic. Methods: In this cross-sectional study, we collected free-living PA data from 94 community-dwelling older women aged between 60 and 96 years (mean age 75.1 years, SD 7.3) using wrist-worn ActiGraph GT9X accelerometers between February and August 2021. We examined whether their daily duration spent in sedentary behavior (SB), light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) varied by sociodemographic characteristics, health and physical function, and COVID-19 related factors. Results: On average, participants accumulated 12.4 (SD 1.9) hours/day in SB, 218.6 (SD 64.3) minutes/day in LPA, and 42.4 (SD 31.0) minutes/day in MVPA, exhibiting overall reduced PA levels than previously published pre–COVID-19 norms of older US women. Among participants aged ≥80 years, sedentary time was 7.5% (P=.003) higher and the time spent in LPA and MVPA was, respectively, 13.3% (P=.03) and 44.9% (P<.001) lower than those aged 60-79 years. More MVPA participation and a less sedentary lifestyle were observed in those who had a higher self-rated health score (MVPA: P=.001, SB: P=.04) and lower fear of falling (FOF; MVPA: P=.003, SB: P=.04). Poorer performance in the 30-second sit-to-stand (STS) test was independently associated with more SB (P=.01) and less LPA (P=.04) and MVPA (P=.001) time among participants. In addition, sedentary time was 5.0% higher (P=.03) in frail and prefrail participants than their healthy counterparts. Conclusions: During the pandemic, older women spent the majority of their waking time being sedentary, while LPA accounted for a larger portion of their daily PA. Therefore, replacing SB with LPA (rather than MVPA) might provide a more feasible PA target for older women, particularly those aged ≥80 years or who have reduced physical function. In addition, targeted interventions might be beneficial in promoting an active lifestyle for those who live alone, are prefrail or frail, and have a high FOF in older age. International Registered Report Identifier (IRRID): RR2-10.2196/27381

The individual effects of physical activity (PA) and sedentary behavior (SB) on health are well-recognized. However, little is known about the extent to which different combinations of these behaviors are associated with body composition and fall risk in older adults. This cross-sectional study examined the associations of mutually exclusive categories of PA and SB with body composition and fall risk in older women. Accelerometer-measured PA, body composition and fall risk (static and dynamic balance) parameters were assessed among 94 community-dwelling older women. The participants were categorized into four groups: active-low sedentary, active-high sedentary, inactive-low sedentary and inactive-high sedentary (active: ≥150 min/week moderate-to-vigorous PA (MVPA); low sedentary: lowest tertile of SB and light PA ratio). Compared to the inactive-high sedentary group, more favorable body composition and dynamic balance results were found in the active-low sedentary (body fat mass index (BFMI): β = −4.37, p = 0.002; skeletal muscle mass index (SMI): β = 1.23, p = 0.017; appendicular lean mass index (ALMI): β = 1.89, p = 0.003; appendicular fat mass index (AFMI): β = −2.19, p = 0.003; sit-to-stand: β = 4.52, p = 0.014) and inactive-low sedentary (BFMI: β = −3.14, p = 0.007; SMI: β = 1.05, p = 0.014; AFMI: β = −1.74, p = 0.005, sit-to-stand: β = 3.28, p = 0.034) groups. Our results suggest that PA programs focusing on concurrently achieving sufficient MVPA and reduced SB might promote a healthy body composition and reduced fall risk among older adults.

This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user’s walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.

Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.

Background: Interdisciplinary evaluation of older adults’ health care is a priority in the prevention of chronic health conditions and maintenance of daily functioning. While many studies evaluate different physical performance tests (PPTs) from a retrospective view in predicting mortality or cardiopulmonary health, it remains unclear which of the commonly used PPTs is the most effective at evaluating the current health of older adults. Additionally, the time and participant burden for each PPT must be considered when planning and implementing them for clinical or research purposes. Objective: This cross-sectional study aimed to determine how elements of overall physical capacity, performance, and other nongait factors in older adults affect the results of 3 commonly used tests: the Short Physical Performance Battery (SPPB), 6-minute walk test (6MWT), and Incremental Shuttle Walk Test (ISWT). Methods: A total of 53 community-dwelling older adults met the inclusion and exclusion criteria (mean age 77.47, SD 7.25 years; n=41, 77% female; and n=21, 40% Hispanic). This study evaluated older adults using 3 different PPTs including the SPPB, 6MWT, and ISWT, as well as constructed multiple linear regression models with measures of physical activity, static balance, and fear of falling (FoF). The nongait measures included 7 days of physical activity monitoring using the ActiGraph GT9X Link instrument, objective measurement of static balance using the BTrackS Balance System, and FoF using the short Fall Efficacy Scale-International. Results: The models revealed that the complete SPPB provided the most comprehensive value, as indicated by a greater R2 value (0.523), and that performance on the SPPB was predicted by both moderate to vigorous physical activity (P=.01) and FoF (P<.001). The ISWT was predicted by moderate to vigorous physical activity (P=.02), BMI (P=.02), and FoF (P=.006) and had a similar R2 value (0.517), whereas the gait component of the SPPB (P=.001) and 6MWT (P<.001) was predicted by only FoF and had lower R2 values (0.375 and 0.228, respectively). Conclusions: The results indicated the value of a multicomponent, comprehensive test, such as the SPPB, in evaluating the health of older adults. Additionally, a comparison of the 2 field walking tests (ISWT and 6MWT) further distinguished the ISWT as more responsive to overall health in older adults. In comparing these commonly used PPTs, clinicians and researchers in the field can determine and select the most optimal test to evaluate older adults in communities and research settings.