Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

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Background: Virtual reality (VR) is becoming a widespread tool in rehabilitation, especially for postural stability. However, the impact of using VR in a “moving wall paradigm” (visual perturbation), specifically without and with anticipation of the perturbation, is unknown. Methods: Nineteen healthy subjects performed three trials of static balance testing on a force plate under three different conditions: baseline (no perturbation), unexpected VR perturbation, and expected VR perturbation. The statistical analysis consisted of a 1 × 3 repeated-measures ANOVA to test for differences in the center of pressure (COP) displacement, 95% ellipsoid area, and COP sway velocity. Results: The expected perturbation rendered significantly lower (p < 0.05) COP displacements and 95% ellipsoid area compared to the unexpected condition. A significantly higher (p < 0.05) sway velocity was also observed in the expected condition compared to the unexpected condition. Conclusions: Postural stability was lowered during unexpected visual perturbations compared to both during baseline and during expected visual perturbations, suggesting that conflicting visual feedback induced postural instability due to compensatory postural responses. However, during expected visual perturbations, significantly lowered postural sway displacement and area were achieved by increasing the sway velocity, suggesting the occurrence of postural behavior due to anticipatory postural responses. Finally, the study also concluded that VR could be used to induce different postural responses by providing visual perturbations to the postural control system, which can subsequently be used as an effective and low-cost tool for postural stability training and rehabilitation.

Background Falls due to postural instability are common in construction environments especially from a height. The purpose of the study was to investigate the impact of virtual reality (VR)-generated environments at different virtual heights on postural stability. Methods Nineteen adults were analyzed for postural stability, tested in real (No VR) environment and in three VR environments, randomly assigned, at virtual heights of 0 ft. (VR0), 40 ft. (VR40), and 120 ft. (VR120). Postural stability was quantified using center of pressure postural sway variables and analyzed using a repeated measures analysis of variance (ANOVA). Participants also completed a simulation sickness questionnaire (SSQ) before and after VR exposure and a presence questionnaire (PQ) after VR exposure. Findings Significant postural instability ( p < .05) was identified between VR and No VR, in which increased postural instability was evident in all VR conditions compared with No VR. Scores from SSQ were within a pre–post score difference of five and the PQ score was (104.21 ± 14.03). Conclusion/Application to Practice Findings suggest that VR environments, regardless of virtual height, induced increased postural instability, which can be attributed to visual sensory conflicts to the postural control system created by VR exposure. Participants’ subjective responses on SSQ and PQ confirmed the feasibility of using VR to represent realistic immersions in virtual heights. However, objectively, VR could potentially lead to postural instability, stressing caution. VR can be a potential tool for providing virtual high-altitude environment exposure for fall prevention training, however, more research is needed on postural adaptation with acute and chronic exposure to VR.

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Background: Wearable technology is used by clinicians and researchers and play a critical role in biomechanical assessments and rehabilitation. Objective: The purpose of this research is to validate a soft robotic stretch (SRS) sensor embedded in a compression knee brace (smart knee brace) against a motion capture system focusing on knee joint kinematics. Methods: Sixteen participants donned the smart knee brace and completed three separate tasks: non-weight bearing knee flexion/extension, bodyweight air squats, and gait trials. Adjusted R2 for goodness of fit (R2), root mean square error (RMSE), and mean absolute error (MAE) between the SRS sensor and motion capture kinematic data for all three tasks were assessed. Results: For knee flexion/extension: R2 = 0.799, RMSE = 5.470, MAE = 4.560; for bodyweight air squats: R2 = 0.957, RMSE = 8.127, MAE = 6.870; and for gait trials: R2 = 0.565, RMSE = 9.190, MAE = 7.530 were observed. Conclusions: The smart knee brace demonstrated a higher goodness of fit and accuracy during weight-bearing air squats followed by non-weight bearing knee flexion/extension and a lower goodness of fit and accuracy during gait, which can be attributed to the SRS sensor position and orientation, rather than range of motion achieved in each task.

Background: Bat velocity, attack angle, and vertical angle are common variables that coaches and players want to evaluate during their baseball or softball swing. Objective: The purpose of this study was to investigate and validate a baseball bat handle sensor against motion capture using recreational baseball and softball athletes for bat velocity, attack angle, and vertical angle. Methods: This single visit cross-sectional experimental design study utilized eighteen recreational baseball and softball players (ten males and eight females, age: 20.70 ± 1.69 years, height: 170.74 ± 5.69 cm, weight: 77.97 ± 12.30 kg) were recruited. Bat velocity, attack angle, and vertical angle from the bat handle sensor and 12-camera motion capture system were collected and compared using a two-tailed paired t-test. Results: Differences were statistically significant, showing that 95% of the time, the bat handle sensor overestimated the bat velocity by 1.92 to 2.77 m/s, underestimated the attack angle by -3.46 to -1.96º, and overestimated the vertical angle by 1.64 to 3.21º, compared to the motion capture system. Conclusion: The bat velocity and vertical angle were overestimated, while the attack angle was underestimated by the bat sensor. The information presented in this study can be viable information for coaches and players when utilizing the baseball bat handle sensor technology for training, practice, or in-game situations.

Postural control is a complex process requiring both sensory and motor responses. Perturbation-based balance training has emerged as an effective fall prevention intervention, which provides physical postural perturbations for postural control training and adaptation. With the advent of technology, virtual reality (VR) has also been used for fall prevention training by providing visual postural perturbations. This article addresses such VR studies, including a recent experiment which involved recreating the classical “moving room” paradigm into a “virtual moving room-wall paradigm” to assess the impact of VR-induced visual postural perturbations on postural stability and control. Evidence of both compensatory and anticipatory postural responses during unexpected and expected visual postural perturbations is presented. The future scope, required virtual environment set-up variations, limitations, and significance of a “virtual moving wall” paradigm in the learning and adaptation of postural control behavior are also discussed.