There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations.
19 July 2016 in JMIR Rehabilitation and Assistive Technologies
Background: Outcome measures for non-ambulant Duchenne muscular dystrophy (DMD) patients are limited, with only the Performance of the Upper Limb (PUL) approved as endpoint for clinical trials. Objective: We assessed four outcome measures based on devices developed for the gaming industry, aiming to overcome disadvantages of observer-dependency and motivation. Methods: Twenty-two non-ambulant DMD patients (range 8.6–24.1 years) and 14 healthy controls (HC; range 9.5–25.4 years) were studied at baseline and 16 patients at 12 months using Leap Motion to quantify wrist/hand active range of motion (aROM) and a Kinect sensor for reached volume with Ability Captured Through Interactive Video Evaluation (ACTIVE), Functional Workspace (FWS) summed distance to seven upper extremity body points, and trunk compensation (KinectTC). PUL 2.0 was performed in patients only. A stepwise approach assessed quality control, construct validity, reliability, concurrent validity, longitudinal change and patient perception. Results: Leap Motion aROM distinguished patients and HCs for supination, radial deviation and wrist flexion (range p = 0.006 to <0.001). Reliability was low and the manufacturer’s hand model did not match the sensor’s depth images. ACTIVE differed between patients and HCs (p < 0.001), correlated with PUL (rho = 0.76), and decreased over time (p = 0.030) with a standardized response mean (SRM) of –0.61. It was appraised as fun on a 10-point numeric rating scale (median 9/10). PUL decreased over time (p < 0.001) with an SRM of –1.28, and was appraised as fun (median 7/10). FWS summed distance distinguished patients and HCs (p < 0.001), but reliability in patients was insufficient. KinectTC differed between patients and HCs (p < 0.01), but correlated insufficiently with PUL (rho = –0.69). Conclusions: Only ACTIVE qualified as potential outcome measure in non-ambulant DMD patients, although the SRM was below the commonly used threshold of 0.8. Lack of insight in technological constraints due to intellectual property and software updates made the technology behind these outcome measures a kind of black box that could jeopardize long-term use in clinical development.
Optimal upper extremity motor control and range of motion are necessary to achieve even the basic activities of daily living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide an assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, the sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at the 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3 months were noted (r = 0.619, r = 0.661, p < 0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r = 0.690, r = 0.815, p < 0.05). Multiple linear regression analyses revealed a change in average bilateral total RSA of 0.1 unit from admission to the 3-month follow-up correlated with a respective change in the FIM self-care score of 2.011 points (95%CI: 0.663–3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.