Journal of NeuroEngineering and Rehabilitation

Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, performance, and control algorithms among the existing robotic platforms make it hard to effectively assess and compare human arm weight relief. In this paper, we introduce criteria for ideal arm weight compensation, and furthermore, we propose and analyze three distinct arm weight compensation methods (Average, Full, Equilibrium) in the arm rehabilitation exoskeleton ’ARMin’. The effect of the best performing method was validated in chronic stroke subjects to increase the active range of motion in three dimensional space.

All three methods are based on arm models that are generalizable for use in different robotic devices and allow individualized adaptation to the subject by model parameters. The first method Average uses anthropometric tables to determine subject-specific parameters. The parameters for the second method Full are estimated based on force sensor data in predefined resting poses. The third method Equilibrium estimates parameters by optimizing an equilibrium of force/torque equations in a predefined resting pose. The parameters for all three methods were first determined and optimized for temporal and spatial estimation sensitivity. Then, the three methods were compared in a randomized single-center study with respect to the remaining electromyography (EMG) activity of 31 healthy participants who performed five arm poses covering the full range of motion with the exoskeleton robot. The best method was chosen for feasibility tests with three stroke patients. In detail, the influence of arm weight compensation on the three dimensional workspace was assessed by measuring of the horizontal workspace at three different height levels in stroke patients.

All three arm weight compensation methods reduced the mean EMG activity of healthy subjects to at least 49% compared with the no compensation reference. The Equilibrium method outperformed the Average and the Full methods with a highly significant reduction in mean EMG activity by 19% and 28% respectively. However, upon direct comparison, each method has its own individual advantages such as in set-up time, cost, or required technology. The horizontal workspace assessment in poststroke patients with the Equilibrium method revealed potential workspace size-dependence of arm height, while weight compensation helped maximize the workspace as much as possible.

Different arm weight compensation methods were developed according to initially defined criteria. The methods were then analyzed with respect to their sensitivity and required technology. In general, weight compensation performance improved with the level of technology, but increased cost and calibration efforts. This study reports a systematic way to analyze the efficacy of different weight compensation methods using EMG. Additionally, the feasibility of the best method, Equilibrium, was shown by testing with three stroke patients. In this test, a height dependence of the workspace size also seemed to be present, which further highlights the importance of patient-specific weight compensation, particularly for training at different arm heights.

ClinicalTrials.gov,NCT02720341. Registered 25 March 2016

Loss of hand function following high level spinal cord injury (SCI) is perceived as a high priority area for rehabilitation. Following discharge, it is often impractical for the specialist care centre to provide ongoing therapy for people living with chronic SCI at home, which can lead to further deterioration of hand function and a direct impact on an individual’s capability to perform essential activities of daily living (ADL).

This pilot study investigated the therapeutic effect of a self-administered home-based hand rehabilitation programme for people with cervical SCI using the soft extra muscle (SEM) Glove by Bioservo Technologies AB.

Fifteen participants with chronic cervical motor incomplete (AIS C and D) SCI were recruited and provided with the glove device to use at home to complete a set task and perform their usual ADL for a minimum of 4 h a day for 12 weeks. Assessment was made at Week 0 (Initial), 6, 12 and 18 (6-week follow-up). The primary outcome measure was the Toronto Rehabilitation Institute hand function test (TRI-HFT), with secondary outcome measures including pinch dynamometry and the modified Ashworth scale.

The TRI-HFT demonstrated improvement in hand function at Week 6 of the therapy including improvement in object manipulation (58.3 ±3.2 to 66.9 ±1.8, p ≈ 0.01), and palmar grasp assessed as the length of the wooden bar that can be held using a pronated palmar grip (29.1 ±6.0 cm to 45.8 ±6.8 cm, p <0.01). A significant improvement in pinch strength, with reduced thumb muscle hypertonia was also detected. Improvements in function were present during the Week 12 assessment and also during the follow-up.

Self-administered rehabilitation using the SEM Glove is effective for improving and retaining gross and fine hand motor function for people living with chronic spinal cord injury at home. Retention of improved hand function suggests that an intensive activity-based rehabilitation programme in specific individuals is sufficient to improve long-term neuromuscular activity. Future studies should characterise the neuromuscular mechanism of action and the minimal rehabilitation programme necessary with the assistive device to improve ADL tasks following chronic cervical SCI.

Trial registration: ISRCTN, ISRCTN98677526, Registered 01/June/2017 - Retrospectively registered, http://www.isrctn.com/ISRCTN98677526

Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment.

Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665).

Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.