PI-Sole: A Low-Cost Solution for Gait Monitoring Using Off-The-Shelf Piezoelectric Sensors and IMU

In this paper, viability of low-cost off-the-shelf Piezoelectric ceramic disc elements is explored for an insole-based gait monitoring system, `PI-Sole' (Piezo In-Sole). Piezoelectric elements can sense dynamic changes in pressure in a closed-loop environment with good sensitivity and a wide measurement range. In this paper, method to enable these elements to continuously sense plantar pressure while walking is proposed, making them a very cost-efficient alternative to the widely used Force Sensing Resistors (FSR) and pressure plates for monitoring human gait. However, piezoelectric elements show hysteresis in their force response, inducing a drift in calculated pressure which increases with time. A novel and effective method to perform detrending of the signal is also presented utilizing stride contexts from a 6-DoF Inertial Measurement Unit (IMU) and the same is utilized to perform zero-correction in the pressure data. 3-D trajectories of strides are calculated using the IMU, and parameters like stride length, stride height etc. are further derived. In order to test the validity of our proposed methods, important kinetic parameters like Vertical Ground Reaction Force (VGRF) and Center of Pressure (CoP) are calculated using PI-Sole and compared to the ones calculated using FSR's in multiple prior works. Applicability of PI-Sole is demonstrated further by depicting and analysing characteristic differences between a heel-strike toe-off stance type, and a flat-strike stance type, the latter being one of the primary symptoms in many cases of pathological gait, including Parkinsonian gait. Important artefacts from foot's height profile while walking are analysed for both stance types in context of standard gait events. We report a mean error of 2.8cm in stride length calculation, and a mean accuracy of 94.5% in calculating swing/stance duration of gait cycles.

TMTDyn: A Matlab package for modeling and control of hybrid rigid–continuum robots based on discretized lumped systems and reduced-order models

A reliable, accurate, and yet simple dynamic model is important to analyzing, designing, and controlling hybrid rigid–continuum robots. Such models should be fast, as simple as possible, and user-friendly to be widely accepted by the ever-growing robotics research community. In this study, we introduce two new modeling methods for continuum manipulators: a general reduced-order model (ROM) and a discretized model with absolute states and Euler–Bernoulli beam segments (EBA). In addition, a new formulation is presented for a recently introduced discretized model based on Euler–Bernoulli beam segments and relative states (EBR). We implement these models in a Matlab software package, named TMTDyn, to develop a modeling tool for hybrid rigid–continuum systems. The package features a new high-level language (HLL) text-based interface, a CAD-file import module, automatic formation of the system equation of motion (EOM) for different modeling and control tasks, implementing Matlab C-mex functionality for improved performance, and modules for static and linear modal analysis of a hybrid system. The underlying theory and software package are validated for modeling experimental results for (i) dynamics of a continuum appendage, and (ii) general deformation of a fabric sleeve worn by a rigid link pendulum. A comparison shows higher simulation accuracy (8–14% normalized error) and numerical robustness of the ROM model for a system with a small number of states, and computational efficiency of the EBA model with near real-time performances that makes it suitable for large systems. The challenges and necessary modules to further automate the design and analysis of hybrid systems with a large number of states are briefly discussed.

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots

Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user–robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user.