Quantification of the relative arm-use in patients with hemiparesis using inertial measurement units

Abstract

Background: The most popular method for measuring upper limb activity is based on accelerometry. However, this method is prone to overestimation and is agnostic to the functional utility of a movement. In this study, we used an inertial measurement unit(IMU)-based gross movement score to quantify arm-use in hemiparetic patients at home. Objectives: (i) Validate the gross movement score detected by wrist-worn IMUs against functional movements identified by human assessors. (ii) Test the feasibility of using wrist-worn IMUs to measure arm-use in patients' natural settings. Methods: To validate the gross movement score two independent assessors analyzed and annotated the video recordings of 5 hemiparetic patients and 10 healthy controls performing a set of activities while wearing IMUs. The second study tracked arm-use of 5 hemiparetic patients and 5 healthy controls using two wrist-worn IMUs for 7 days and 3 days, respectively. The IMU data obtained from this study was used to develop quantitative measures (total and relative arm-use (RAU)) and a visualization method for arm-use. Results: The gross movement score detects functional movement with 50-60% accuracy in hemiparetic patients, and is robust to non-functional movements. Healthy controls showed a slight bias towards the dominant arm (RAU: 40.52)°. Patients' RAU varied between 15-47° depending upon their impairment level and pre-stroke hand dominance. Conclusions: The gross movement score performs moderately well in detecting functional movements while rejecting non-functional movements. The patients' total arm-use is less than healthy controls, and their relative arm-use is skewed towards the less-impaired arm.

A Framework for Sensor-based Assessment of Upper-limb Functioning

“…For instance, among the three measures currently used in the literature, AC and GM have low accuracy due to low specificity and sensitivity, respectively [24]. Machine learning-based approaches such as the random forests classifier appear to perform much better than AC an GM [10, 15].…”

“…All data presented in this section were obtained from a previous study by David et. al [10]. The measurements were obtained from IMUs donned on each wrist, i.e.…”

“…4(b) and Fig. 4(c) display the UVI scatter plots for a healthy and stroke participant, respectively, using data collected from a single day (6 to 8 hours) of recording [10]. For the healthy subject, most points are of short to medium duration and low intensity in Fig.…”

“…Visualizing the relative use of the upper-limbs has been explored through 2D scatter plots or heat-maps of different variables related to the use of the two upper-limbs [8, 10]. Relative upper-limb use can be visualized and quantified using measures of the windowed upper-limb use , windowed upper-limb intensity or windowed upper-limb activity ; here, we use windowed upper-limb intensity for demonstration purposes.…”

“…activity counting (AC) [8, 11, 20]) or (b) the duration of functional movements detected from sensor data (e.g. gross movement (GM) score [10, 13], machine learning (ML) algorithms [14–16]). Although related, movement duration and intensity convey slightly different information about the nature of arm use.…”