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A Wearable Resistance Devices Motor Learning Effects in Exercise

Eugenio Frias-Miranda, Hong-Anh Nguyen, Jeremy Hampton, Trenner Jones, Benjamin Spotts, Matthew Cochran, Deva Chan, Laura H Blumenschein

TL;DR

This work investigates whether passive wearable resistance devices can influence motor learning during exercise. It develops a stretch-sensor-based method to measure forces from a wearable resistance device, establishes a calibration model for the force field, and tests effects on overhead-squat form under no feedback, resistance feedback, and visual feedback. Results show that passive force fields can improve certain learning-related asymmetry metrics and perform comparably to visual feedback for some measures, though not all metrics show consistent gains and retention is limited. These findings suggest passive WR devices could supplement or replace some active feedback in practical training and rehabilitation, with further work needed to optimize force-field design and population scope.

Abstract

The integration of technology into exercise regimens has emerged as a strategy to enhance normal human capabilities and return human motor function after injury or illness by enhancing motor learning and retention. Much research has focused on how active devices, whether confined to a lab or made into a wearable format, can apply forces at set times and conditions to optimize the process of learning. However, the focus on active force production often forces devices to either be confined to simple movements or interventions. As such, in this paper, we investigate how passive device behaviors can contribute to the process of motor learning by themselves. Our approach involves using a wearable resistance (WR) device, which is outfitted with elastic bands, to apply a force field that changes in response to a person's movements while performing exercises. We develop a method to measure the produced forces from the device without impeding the function and we characterize the device's force generation abilities. We then present a study assessing the impact of the WR device on motor learning of proper squat form compared to visual or no feedback. Biometrics such as knee and hip angles were used to monitor and assess subject performance. Our findings indicate that the force fields produced while training with the WR device can improve performance in full-body exercises similarly to a more direct visual feedback mechanism, though the improvement is not consistent across all performance metrics. Through our research, we contribute important insights into the application of passive wearable resistance technology in practical exercise settings.

A Wearable Resistance Devices Motor Learning Effects in Exercise

TL;DR

This work investigates whether passive wearable resistance devices can influence motor learning during exercise. It develops a stretch-sensor-based method to measure forces from a wearable resistance device, establishes a calibration model for the force field, and tests effects on overhead-squat form under no feedback, resistance feedback, and visual feedback. Results show that passive force fields can improve certain learning-related asymmetry metrics and perform comparably to visual feedback for some measures, though not all metrics show consistent gains and retention is limited. These findings suggest passive WR devices could supplement or replace some active feedback in practical training and rehabilitation, with further work needed to optimize force-field design and population scope.

Abstract

The integration of technology into exercise regimens has emerged as a strategy to enhance normal human capabilities and return human motor function after injury or illness by enhancing motor learning and retention. Much research has focused on how active devices, whether confined to a lab or made into a wearable format, can apply forces at set times and conditions to optimize the process of learning. However, the focus on active force production often forces devices to either be confined to simple movements or interventions. As such, in this paper, we investigate how passive device behaviors can contribute to the process of motor learning by themselves. Our approach involves using a wearable resistance (WR) device, which is outfitted with elastic bands, to apply a force field that changes in response to a person's movements while performing exercises. We develop a method to measure the produced forces from the device without impeding the function and we characterize the device's force generation abilities. We then present a study assessing the impact of the WR device on motor learning of proper squat form compared to visual or no feedback. Biometrics such as knee and hip angles were used to monitor and assess subject performance. Our findings indicate that the force fields produced while training with the WR device can improve performance in full-body exercises similarly to a more direct visual feedback mechanism, though the improvement is not consistent across all performance metrics. Through our research, we contribute important insights into the application of passive wearable resistance technology in practical exercise settings.
Paper Structure (15 sections, 1 equation, 9 figures, 2 tables)

This paper contains 15 sections, 1 equation, 9 figures, 2 tables.

Figures (9)

  • Figure 1: An individual performing an overhead squat while wearing a wearable resistance (WR) device. The device applies force feedback through resistance bands. As the user squats down, the resistance force decreases.
  • Figure 2: Illustration of the wearable resistance (WR) device: (lower left) a cross-sectional view of one of the sides showcasing the resistance band wrapping around the pulleys and the knee/wrist clips, (right) an isometric view of the WR device is shown with the resistance bands presented as a series of springs showing the initial length of the location of the sewing of the markers $\ell_{0}$, the difference between the length of the motion capture markers throughout the exercises and length of the motion capture markers at rest $\Delta \ell$, and the average between both of the band's calibration stiffness $k_{cal}$.
  • Figure 3: The calibration experiments experimental setup (a) and calibration results of the stretch sensor (b). (a) The force gauge (Series 7, Mark-10 Corporation, NY, USA), camera, AprilTag markers placement, wrist clip, and knee clip. (b) The force-displacement relationship for the stretch sensor, with separate lines representing the left $(F = 5.33*\Delta \ell + 4.86[N])$ and right $(F = 5.61*\Delta \ell + 3.03[N])$ sensors. The x-axis is displacement in cm, and the y-axis is force in N.
  • Figure 4: (a) force vector plot of wearable resistance (WR) device while performing anatomical plane exercises (coronal, transverse, and sagittal) (b) force vector plot of WR device while performing overhead squats with good and poor form (c) force vector plot of WR device while performing extended arm lunges, with good and poor form (d) resistance band force over exercise completion percentage for anatomical plane exercises (e) resistance band force over squat completion percentage (f) resistance band force over lunge completion percentage
  • Figure 5: Diagram of the study's timeline which includes: session 1 the screening, session 2 training, and session 3 retention. Session 2 involves performing a series of overhead squats: 1 set of 10 reps (baseline), 15 sets of 5 reps (training), and 1 set of 10 reps (post-training). Feedback methods for the training section are no feedback, resistance feedback, and visual feedback. The evaluated biometrics throughout the study are defined: pelvic obliquity, and knee/hip angles.
  • ...and 4 more figures