Use of Wearable Sensor Device and Mobile Application for Objective Assessment of Pain in Post-surgical Patients: A Preliminary Study.

Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g.

IMU-based assistance modulation in upper limb soft wearable exosuits.

Soft exosuits have advantages over their rigid counterparts in terms of portability, transparency and ergonomics. Our previous work has shown that a soft, fabric-based exosuit, actuated by an electric motor and a Bowden cable, reduced the muscular effort of the user when flexing the elbow. This previous exosuit used a gravity compensation algorithm with the assumption that the shoulder was adducted at the trunk.

An LDA-Based Approach for Real-Time Simultaneous Classification of Movements Using Surface Electromyography.

Myoelectric-based decoding strategies offer significant advantages in the areas of human-machine interactions because they are intuitive and require less cognitive effort from the users. However, a general drawback in using machine learning techniques for classification is that the decoder is limited to predicting only one movement at any instant and hence restricted to performing the motion in a sequential manner, whereas human motor control strategy involves simultaneous actuation of multiple degrees of freedom (DOFs) and is considered to be a natural and efficient way of performing tasks. Simultaneous decoding in the context of myoelectric-based movement control is a challenge that is being addressed recently and is increasingly popular.

The Influence of External Forces on Wrist Proprioception.

Proprioception combines information from cutaneous, joint, tendon, and muscle receptors for maintaining a reliable internal body image. However, it is still a matter of debate, in both neurophysiology and psychology, to what extent such body image is modified or distorted by a changing haptic environment. In particular, what is worth investigating is the contribution of external forces on our perception of body and joint configuration.

Position and stiffness modulation of a wrist haptic device using myoelectric interface.

Modulation of stiffness provides a great deal of advantage in the way humans interact with the environment, and is very important in successfully performing activities of daily living. In the context of human-machine interactions, stiffness control could provide a safer interaction, especially when dealing with unpredictable environment. In this paper we propose a user-modulated stiffness and position control for the wrist flexion/extension degree of freedom while physically coupled to a haptic device.

Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living.

The development of a portable assistive device to aid patients affected by neuromuscular disorders has been the ultimate goal of assistive robots since the late 1960s. Despite significant advances in recent decades, traditional rigid exoskeletons are constrained by limited portability, safety, ergonomics, autonomy and, most of all, cost. In this study, we present the design and control of a soft, textile-based exosuit for assisting elbow flexion/extension and hand open/close.

Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines.

Background: Myoelectric signals offer significant insights in interpreting the motion intention and extent of effort involved in performing a movement, with application in prostheses, orthosis and exoskeletons. Feature extraction plays a vital role, and follows two approaches: EMG and synergy features. More recently, muscle synergy based features are being increasingly explored, since it simplifies dimensionality of control, and are considered to be more robust to signal variations.

Muscle synergies for reliable classification of arm motions using myoelectric interface.

Synergistic activation of muscles are considered to be the phenomenon by which the central nervous system simplifies its control strategy. Muscle synergies are neurally encoded and considered robust to be able to adapt for various external dynamics. This paper presents a myoelectric-based interface to identify and classify motions of the upper arm involving the shoulder and elbow.

Embedded human control of robots using myoelectric interfaces.

Myoelectric controlled interfaces have become a research interest for use in advanced prostheses, exoskeletons, and robot teleoperation. Current research focuses on improving a user's initial performance, either by training a decoding function for a specific user or implementing "intuitive" mapping functions as decoders. However, both approaches are limiting, with the former being subject specific, and the latter task specific.