A method to qualitatively assess arm use in stroke survivors in the home environment

Abstract

Wearable sensor technology has enabled unobtrusive monitoring of arm movements of stroke survivors in the home environment. However, the most widely established method, based on activity counts, provides quantitative rather than qualitative information on arm without functional insights, and is sensitive to passive arm movements during ambulatory activities. We propose a method to quantify functionally relevant arm use in stroke survivors relying on a single wrist-worn inertial measurement unit. Orientation of the forearm during movements is measured in order identify gross arm movements. The method is validated in 10 subacute/chronic stroke survivors wearing inertial sensors at 5 anatomical locations for 48 h. Measurements are compared to conventional activity counts and to a test for gross manual dexterity. Duration of gross arm movements of the paretic arm correlated significantly better with the Box and Block Test () than conventional activity counts when walking phases were included (), and similar results were found when comparing ratios of paretic and non-paretic arms for gross movements and activity counts. The proposed gross arm movement metric is robust against passive arm movements during ambulatory activities and requires only a single-sensor module placed at the paretic wrist for the assessment of functionally relevant arm use.

Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units

“…Only features from the time domain were taken for further analysis as we have shown that in “real world” applications, frequency domain features usually do not provide useful information ( 40 ). In addition, some high level features were included: AC ( 41 ), total distance traveled and distance traveled actively ( 40 ), altitude difference within one epoch, altitude variance within one epoch, and time above acceleration magnitude threshold (empirically chosen). In order to test whether the inclusion of HR improves the overall accuracy of EE estimation, as shown by Nightingale et al ( 42 ), mean HR, resting HR and the difference between mean HR and resting HR were included as additional features.…”

Inertial Sensor Measurements of Upper-Limb Kinematics in Stroke Patients in Clinic and Home Environment

“…Furthermore, the presence of the therapist could influence the patient performance during the measurement. A reduced sensor set would improve the problem of obtrusiveness (Leuenberger et al, 2016 ; van Meulen et al, 2017 ).…”

“…In stroke clinical trials, acceleration sensors have been used to measure the patient arm-activities in real world (Uswatte et al, 2005 ). Although accelerometer sensors can be used to measure movements in the sagittal plane (Leuenberger et al, 2016 ), they cannot provide information regarding three-dimensional (3D) movements of the upper limb. To measure movement quality kinematic metrics from optical motion capture systems quantify the patients’ motor abilities on a body function level but remain restricted to a motion capture laboratory and cannot be used in daily life.…”

Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

“…For activity tracking, a single 6 DOF IMU sensor with complex feature extraction and motion separation algorithms are necessary to detect different movements and identify activities. More complex motion tracking, such as detection of limb and body part movements and posture is possible through securing multiple IMUs with important clinical applications from lower back pain [ 48 ] to stroke rehabilitation [ 49 ] and gait monitoring [ 50 ]. For example, Hajibozorgi et al [ 48 ] have used multiple IMUs placed on the back to monitor posture and range of motion (ROM) of spine.…”