Design and implementation of a low-cost mechatronic shoe for biomechanical analysis of the human locomotion

In this paper the development of a low-cost and easy wearable mechatronic system for the measurement of ground reaction forces (GRF) for the biomechanical analysis of the human locomotion is presented. The system consists of an insole, a conditioning device for the signals produced by the sensors applied to the insole and a data acquisition system connected to a USB portable storage. The sensors applied to the insole can measure the reaction forces in the horizontal and vertical directions during locomotion. The prototype was validated by comparing the data from the sensors with the values obtained using a force platform

Smart garment for trunk posture monitoring: A preliminary study

© 2008 Wong and Wong; licensee BioMed Central Ltd

Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

BACKGROUND: The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. METHODS: The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. RESULTS: The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. CONCLUSION: Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice

A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors

Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors