Abstract
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.
Highlights
Falls are one of the leading causes of both fatal and nonfatal injuries in clinical [1], geriatric [1], occupational [2], and healthy athletic populations [3] and can be induced due to environmental factors as well as physical and psychological human factors [4]
Results from this study indicate that the stretch sensors could be used as a feasible option in detecting falls during slips and trips, even when they are unexpected or expected and across both left and right foot–ankle segments
Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model, suggesting a moderate to high accuracy and minimal errors in comparing soft robotic sensor (SRS)
Summary
Falls are one of the leading causes of both fatal and nonfatal injuries in clinical [1], geriatric [1], occupational [2], and healthy athletic populations [3] and can be induced due to environmental factors as well as physical and psychological human factors [4]. Wearable sensors have been used for human activity monitoring in various fields such as sports, training, fitness for improving performance, and preventing injuries and have been used successfully in monitoring physical activity in clinical, pathological, and aging populations [5]. ; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations
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