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

Sensors

Vicky Chan¹  DavidJ. Reinkensmeyer¹  Justin Rowe²  Diogo Schwerz de Lucena³ 

[1] Department of Mechanical and Aerospace Engineering, Department of Anatomy and Neurobiology, University of California Irvine, Irvine, CA 92697, USA;Flint Rehabilitation Devices, Irvine, CA 92614, USA;John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;

关键词: wearable sensing;    IMU;    hand movement;    dexterity;    rehabilitation;    stroke;   

DOI  :  10.3390/s21041502

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown