Development of a Bendable Outsole Biaxial Ground Reaction Force Measurement System

MEMS-Based Micro Sensors for Measuring the Tiny Forces Acting on Insects

Small insects perform agile locomotion, such as running, jumping, and flying. Recently, many robots, inspired by such insect performance, have been developed and are expected to be smaller and more maneuverable than conventional robots. For the development of insect-inspired robots, understanding the mechanical dynamics of the target insect is important. However, evaluating the dynamics via conventional commercialized force sensors is difficult because the exerted force and insect itself are tiny in strength and size. Here, we review force sensor devices, especially fabricated for measuring the tiny forces acting on insects during locomotion. As the force sensor, micro-force plates for measuring the ground reaction force and micro-force probes for measuring the flying force have mainly been developed. In addition, many such sensors have been fabricated via a microelectromechanical system (MEMS) process, due to the process precision and high sensitivity. In this review, we focus on the sensing principle, design guide, fabrication process, and measurement method of each sensor, as well as the technical challenges in each method. Finally, the common process flow of the development of specialized MEMS sensors is briefly discussed.

Pneumatic AFO Powered by a Miniature Custom Compressor for Drop Foot Correction

For active AFO applications, pneumatic remote transmission has advantages in minimizing the mass and complexity of the system due to the flexibility in placing pneumatic components and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors, which greatly limit the practical daily usage. In this study, we implemented a wearable custom compressor that can be worn at the trunk of the body and can generate up to 1050 kPa of pressurized air to power an unilateral active AFO for dorsiflexion (DF) assistance of drop-foot patients. In order to minimize the size and weight of the custom compressor, the compression rate of the custom compressor was optimized to the rate of consumption required to power the active AFO. The finalized system can provide a maximum assistive torque of 9.8 Nm at a functional frequency of 1 Hz and the average resistive torque during free movement was 0.03 Nm. The system was tested for five hemiplegic drop-foot patients. The proposed system showed an average improvement of 12.3° of ankle peak dorsiflexion angle during the mid to late swing phase.

An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data

This study aimed to develop a wearable sensor system, using machine-learning models, capable of accurately estimating peak ground reaction force (GRF) during ballet jumps in the field. Female dancers (n = 30) performed a series of bilateral and unilateral ballet jumps. Dancers wore six ActiGraph Link wearable sensors (100 Hz). Data were collected simultaneously from two AMTI force platforms and synchronised with the ActiGraph data. Due to sensor hardware malfunctions and synchronisation issues, a multistage approach to model development, using a reduced data set, was taken. Using data from the 14 dancers with complete multi-sensor synchronised data, the best single sensor was determined. Subsequently, the best single sensor model was refined and validated using all available data for that sensor (23 dancers). Root mean square error (RMSE) in body weight (BW) and correlation coefficients (r) were used to assess the GRF profile, and Bland–Altman plots were used to assess model peak GRF accuracy. The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95; bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80; bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps.