Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

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.

Closing the Wearable Gap—Part IV: 3D Motion Capture Cameras Versus Soft Robotic Sensors Comparison of Gait Movement Assessment

“…Initially, the analysis was performed one-at-a-time on a single sensor basis in a similar manner as previous studies [9,10]. In this approach, linear models were generated for each individual sensor and compared to its corresponding movement (i.e., PF and DF sensors modeled to motion capture flexion; INV and EVR sensors modeled to motion capture inversion).…”

“…Table 1 provides a summary of the key performance statistics of the experiment. In addition to using the root mean square error (RMSE) and adjusted R 2 to determine measurement performance as was done for previous experiments [9,10], the mean absolute error (MAE) was added as a performance metric. MAE was found to be a desirable metric as it still provides a measure of the prediction performance of the stretchable SRS but-unlike RMSE-it does not add significant weight to large errors.…”

“…In Parts II and III of "closing the wearable gap" paper series [9,10], both 3D motion capture and SRS were used to assess specific foot-ankle movements. Part II analyzed the four primary foot-ankle complex angles-plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)-during static movement assessments where each angle was measured separately [9].…”

“…Part II analyzed the four primary foot-ankle complex angles-plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)-during static movement assessments where each angle was measured separately [9]. Part III captured PF and DF during dynamic slip and trip movements that were both expected and unexpected [10]. For Part IV of this series, all four foot-ankle movements will be captured simultaneously during dynamic gait trials.…”

“…For Part IV of this series, all four foot-ankle movements will be captured simultaneously during dynamic gait trials. Therefore, due to similarities in equipment, study design, and discovery intent, much of the layout and wording of this section will originate from the previous papers [9,10] with modifications to describe (a) the new population of participants and (b) modifications in the methods to shift from Electronics 2019, 8, 1382 3 of 16 single sensors capturing static movements and two sensors capturing dynamic slips and trips to four sensors per leg capturing dynamic gait movements.…”