Magnetically Counting Hand Movements: Validation of a Calibration-Free Algorithm and Application to Testing the Threshold Hypothesis of Real-World Hand Use after Stroke

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1502
• Main Authors: Schwerz de Lucena, Diogo, Rowe, Justin, Chan, Vicky, Reinkensmeyer, David
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.02.2021; MDPI
• Subjects: Algorithms; Calibration; dexterity; Experiments; Feedback; Female; Hand; hand movement; Humans; Hypotheses; IMU; Laboratories; Magnetic fields; Male; Movement; Noise; Reading; rehabilitation; Stroke; Stroke - diagnosis; Stroke Rehabilitation; wearable sensing; Wrist; IMU; dexterity; rehabilitation; wearable sensing; stroke; hand movement
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21041502

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the “Manumeter”). The “HAND” (Hand Activity estimated by Nonlinear Detection) algorithm assigns a “HAND count” by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants’ Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.