PARTICIPATORY DESIGN OF PEDIATRIC UPPER LIMB PROSTHESES: QUALITATIVE METHODS AND PROTOTYPING.

Objectives: The study aims to develop an understanding of the views of children and adolescents, parents, and professionals on upper limb prosthetic devices to develop and improve device design. Previous research has found that children are dissatisfied with prostheses but has relied heavily on parent proxy reports and quantitative measures (such as questionnaires) to explore their views.

Methods: Thirty-four participants (eight children aged 8-15 years with upper limb difference, nine parents, eight prosthetists, and nine occupational therapists) contributed to the development of new devices through the BRIDGE methodology of participatory design, using focus groups and interviews.

Surface texture detection with artificial fingers.

This paper highlights the potential of using prosthetic devices to sense surface textures; an important characteristic of a lower arm that is often neglected. An artificial finger equipped with a piezoelectric sensor, mounted on a fingertip, has been designed to detect surface textures of different dimensions. Signal frequencies generated during the exploratory movement of the artificial finger reliably correlate to all the widths of grooves and ridges of the surface textures under investigation.

Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles With MARG Sensors.

This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis.

Effect of Trunk Support on Upper Extremity Function in People With Chronic Stroke and People Who Are Healthy.

Background: Trunk control is thought to contribute to upper extremity (UE) function. However, this common assumption in neurorehabilitation has not been validated in clinical trials.

Objective: The study objectives were to investigate the effect of providing external trunk support on trunk control and UE function and to examine the relationship between trunk control and UE function in people with chronic stroke and people who were healthy.

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification.

In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g.