The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching

Abstract

Compensatory movements at the trunk are commonly utilized during reaching by persons with motor impairments due to neurological injury such as stroke. Recent low-cost motion sensors may be able to measure trunk compensation, but their validity and reliability for this application are unknown. The purpose of this study was to compare the first (K1) and second (K2) generations of the Microsoft Kinect to a video motion capture system (VMC) for measuring trunk compensation during reaching. Healthy participants (n = 5) performed reaching movements designed to simulate trunk compensation in three different directions and on two different days while being measured by all three sensors simultaneously. Kinematic variables related to reaching range of motion (ROM), planar reach distance, trunk flexion and lateral flexion, shoulder flexion and lateral flexion, and elbow flexion were calculated. Validity and reliability were analyzed using repeated-measures ANOVA, paired t-tests, Pearson’s correlations, and Bland-Altman limits of agreement. Results show that the K2 was closer in magnitude to the VMC, more valid, and more reliable for measuring trunk flexion and lateral flexion during extended reaches than the K1. Both sensors were highly valid and reliable for reaching ROM, planar reach distance, and elbow flexion for all conditions. Results for shoulder flexion and abduction were mixed. The K2 was more valid and reliable for measuring trunk compensation during reaching and therefore might be prioritized for future development applications. Future analyses should include a more heterogeneous clinical population such as persons with chronic hemiparetic stroke.

Validity and reliability of the single camera marker less motion capture system using RGB-D sensor to measure shoulder range-of-motion: A protocol for systematic review and meta-analysis

“…[12] In addition, several studies have found Kinect V2 to be more valid and reliable for measuring ROM. [5,13] Studies on healthy subjects and patients with shoulder disorders or upper limb motor disorder in all ages will be included.…”

Clinical and pathophysiological foundations and advanced developments in the rehabilitation of patients after ischemic stroke

“…Тогда можно прибегнуть к системе анализа движений тела, которая использует камеру и обычный ноутбук, анализирующий поступающие данные. Например, система Kinect разработана на базе интерфейса видеоигры и по результатам исследований признана эффективной в реабилитации пациентов с двигательными нарушениями [23]. Бюджетным вариантом также можно считать использование интерфейса «мозг-компьютер».…”

Measurement Method of Human Lower Limb Joint Range of Motion Through Human-Machine Interaction Based on Machine Vision

“…The Kinect could extract the temporal and spatial parameters of human gait, which does not need to accurately represent the human bones and limb segments, which solves the problem of event monitoring, such as the old people fall risk (Dubois and Bresciani, 2018). Based on a virtual triangulation method, an evaluation system for shoulder motion of patients based on the Kinect V2 sensors is designed, which can solve the solution of a single shoulder joint motion range of patients at one time (Cai et al, 2019;Çubukçu et al, 2020;Foreman and Engsberg, 2020). However, how to improve the efficiency of a multi-joint range of motion measurement combined with the teaching traction training method of the rehabilitation physician, how to use a single camera to accurately solve the problem of multi-joints spatial motion evaluation of human lower limbs, and how to avoid camera occlusion in the operation process of the rehabilitation physicians are an important basis for accurate input of lower limb motion information of rehabilitation robot.…”