Rehabilitative and assistive wearable mechatronic upper-limb devices: A review.

Recently, there has been a trend toward assistive mechatronic devices that are wearable. These devices provide the ability to assist without tethering the user to a specific location. However, there are characteristics of these devices that are limiting their ability to perform motion tasks and the adoption rate of these devices into clinical settings. The objective of this research is to perform a review of the existing wearable assistive devices that are used to assist with musculoskeletal and neurological disorders affecting the upper limb. A review of the existing literature was conducted on devices that are wearable, assistive, and mechatronic, and that provide motion assistance to the upper limb. Five areas were examined, including sensors, actuators, control techniques, computer systems, and intended applications. Fifty-three devices were reviewed that either assist with musculoskeletal disorders or suppress tremor. The general trends found in this review show a lack of requirements, device details, and standardization of reporting and evaluation. Two areas to accelerate the evolution of these devices were identified, including the standardization of research, clinical, and engineering details, and the promotion of multidisciplinary culture. Adoption of these devices into their intended application domains relies on the continued efforts of the community.

Enhancing the Therapist-Device Relationship: Software Requirements for Digital Collection and Analysis of Patient Data.

Improving upon the therapist-device relationship is an important aspect that will increase the number of upper-limb robotic rehabilitation devices being used for therapy. One path to strengthen this relationship is for these devices to generate large data sets that rehabilitation therapists can use to enhance their patient assessment procedures. In this article, a national survey of Canadian therapists was conducted in order to learn about their data collection and analysis methods. A total of 33 responses were gathered from an online survey. These results show that there is a demand for the collection and visualization of various patient data, some of which cannot be easily collected with existing methods. It was also seen that there exists a large variation between therapists about which major steps constitute the general rehabilitation process. From these results, a set of fourteen general software requirements has been created. Insights from the survey regarding influences on software designs are briefly discussed. This research helps to enable the development of software systems that increase the interaction potential between therapists and robotic devices.

A Wearable Mechatronic Glove for Resistive Hand Therapy Exercises.

Recent technological improvements are consistently improving the efficacy of wearable mechatronic devices designed to support rehabilitation. However, it has been identified that there is currently a limited number of devices that can perform resistive motion tasks. To address this limitation, a Wearable Mechatronics-Enabled (WearME) Glove has been developed to support rehabilitative motion tasks. Using the WearME Glove, a control system was developed to enable the performance of resistive finger and wrist motion tasks. An initial evaluation of the device applied to rehabilitation tasks shows that average control errors of 2.4% and 1.5% were achieved for a resistive finger task and a resistive wrist flexion-extension task, respectively. In addition, an analysis of each task showed that for the index finger, the thumb and the wrist motion, an average of 69%, 76% and 83% of the duration, respectively, were being resisted by the WearME Glove. The results of this study show that the WearME glove can provide consistent resistance to the finger and wrist for different rehabilitation tasks.

Evaluating Muscle Activation Models for Elbow Motion Estimation.

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67-2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.