Scalable 4-D printed tactile sensor for the detection of shear forces in the aid of plantar measurements

A variety of sensing technologies have been proposed to measure loading on the plantar surface of the human foot. The majority have a single measurement axis, and few are designed with multiple measurement axes capable of monitoring both normal and shear stress. In this paper a low cost, biocompatible triaxial sensitive force sensor that can be implemented with a simple fabrication using inexpensive equipment is proposed

Development of a low-profile planar sensor for the detection of normal and shear forces

Individuals with balance and mobility problems might benefit by the use of devices that detect small changes in ground reaction forces and potentially be used to assist movement. For maximum effectiveness, such sensors must measure pressure in all three dimensions. Impact and shear plantar force are essential variables in inverse dynamics reconstructions of the human joint force. Various force sensors have been proposed to monitor plantar forces of the human foot. Most of them have a single-axis measurement, and few are intended for monitoring normal and shear stress. This article proposes a low-cost, biocompatible triaxial piezoresistive sensor developed using simple fabrication techniques and inexpensive machinery. The sensor can detect pressures from 0-800kPa with high response and recovery with minimum hysteresis and repeatable results of over than 100 cycles

Correlating Vibration Patterns to Perception of Tactile Information for Long-Term Prosthetic Limb Use and Continued Rehabilitation of Neuropathic Pain

Prosthetic limbs (and orthotic devices) have been used as a paradigm for the treatment and rehabilitation of neuropathic pain, such as phantom limb pain. Long-term adoption of the devices for the continued use in rehabilitation remains low in part due to reduced embodiment and the high cognitive load associated with controlling the device. Previous research has shown that incorporating sensory feedback in prostheses can provide proprioceptive information, increase control and manipulation of objects, and improve embodiment. However, feedback experienced by the user varies daily and requires constant parameter adjustments to maintain accurate and intuitive sensory perception, further preventing long term adoption. Work therefore needs to be explored that correlate feedback modalities to perception of tactile information, such as texture and pressure. The work presented in this paper begins to explore this by utilizing a deep-learning algorithm to classify the dissipation of vibration artefacts found in the EMG signals of able-bodied individuals to specific texture patterns. Four texture patterns were applied to 7 participants using two vibration motors and repeated 3 times. In post processing, a RNN network identified the artefact features along equidistantly spaced EMG electrodes and correctly classified unseen data from each participant

AI enhanced collaborative human-machine interactions for home-based telerehabilitation

The use of robots in a telerehabilitation paradigm could facilitate the delivery of rehabilitation on demand while reducing transportation time and cost. As a result, it helps to motivate patients to exercise frequently in a more comfortable home environment. However, for such a paradigm to work, it is essential that the robustness of the system is not compromised due to network latency, jitter, and delay of the internet. This paper proposes a solution to data loss compensation to maintain the quality of the interaction between the user and the system. Data collected from a well-defined collaborative task using a virtual reality (VR) environment was used to train a robotic system to adapt to the users' behaviour. The proposed approach uses nonlinear autoregressive models with exogenous input (NARX) and long-short term memory (LSTM) neural networks to smooth out the interaction between the user and the predicted movements generated from the system. LSTM neural networks are shown to learn to act like an actual human. The results from this paper have shown that, with an appropriate training method, the artificial predictor can perform very well by allowing the predictor to complete the task within 25 s versus 23 s when executed by the human

Effects of Focal Vibration and Robotic Assistive Therapy on Upper Limb Spasticity in incomplete Spinal Cord Injury

Vibration stimulation seems to be an affordable easy-to-use rehabilitation tool. Focal muscle vibration (FV) has potential to reduce spasticity and enhance muscle strength and performance. Combined with robotic assisted movement therapy, the rehabilitation can benefit from improvement of more than one aspect. For example, FV could firstly decrease abnormally increased muscle tone and joint rigidity by tackling volitional control for easier robotic movement exercise. Exactly this approach is evaluated within a clinical trial presented in this paper. FV were applied to relaxed spastic wrist flexor and extensor muscles for 15min. Subsequently, the wrist was engaged in a robotic-assisted game-playing. Results from two cases who completed the trial showed short-term decrease in wrist stiffness as assessed by clinical spasticity measurement Modified Ashworth Scale (MAS). Active range of motion (AROM) and engineering joint stiffness (JS) measurements were estimated using a robotic apparatus and the results complemented previous observations. The AROM increased and JS decreased for both cases when compared at the beginning and at the end of each interventional session. These results are a part of an ongoing clinical trial but show promise for reducing repercussions of spasticity in incomplete spinal cord injury

The Effect of Focused Vibration on Tibialis Anterior Function in Normal Subjects

Category: Basic Sciences/Biologics; Other Introduction/Purpose: Foot drop is a debilitating condition which may be caused by peripheral nerve pathology. Focused vibration therapy (FVT) is a novel treatment in this setting which aims to improve the response of muscle motor units to nervous stimuli, improving muscle function and rehabilitation potential. This study investigates the effect of FVT on tibialis anterior (TA) function in normal subjects to determine the optimal timing of application. Methods: Fifteen normal subjects with mean age 25.47±3.27 years and BMI of 21.93±1.54kg/m 2 were recruited. Using surface electromyography (EMG), a rotary potentiometer, video analysis (KINOVEA software), and force gauges, the activity of TA, ankle range of movement (ROM), and force at maximal isometric voluntary contraction (MVC) in dorsiflexion were measured. Baseline TA strength was recorded, and participants then performed a series of dorsiflexion exercises to induce fatigue. Strength measurements were repeated after a 5-minute rest period (control). This process was repeated with FVT applied before exercise (FVBE) and after exercise (during the rest period, FVAE). FVT was delivered over the TA muscle belly at 75Hz / 0.4mm amplitude. Data was analysed using ANOVA. Results: There was no significant change in EMG readings or dorsiflexion ROM between baseline, control and FVBE/FVAE measurements. Compared to baseline, MVC increased by 10.87N (6.6%) after FVBE (p=0.034) and 13.87N (8.4%) after FVAE (p=0.034). Compared to control, MVC did not increase significantly with FVBE, but did increase by 7.70N (4.3%) following FVAE (p=0.049). Conclusion: FVT improved the MVC of TA - this was most pronounced when FVT was applied during the recovery phase, after exercise. The lack of EMG / ROM improvement is expected in normal subjects. Our results suggest that FVT is more effective than passive rest and may enhance muscle strength recovery. Further work will investigate the optimal dose of FVT and response in patients with pathology