Human motion component and envelope characterization via wireless wearable sensors

Abstract

Background: The characterization of limb biomechanics has broad implications for analyzing and managing motion in aging, sports, and disease. Motion capture videography and on-body wearable sensors are powerful tools for characterizing linear and angular motions of the body, though are often cumbersome, limited in detection, and largely non-portable. Here we examine the feasibility of utilizing an advanced wearable sensor, fabricated with stretchable electronics, to characterize linear and angular movements of the human arm for clinical feedback. A wearable skin-adhesive patch with embedded accelerometer and gyroscope (BioStampRC, MC10 Inc.) was applied to the volar surface of the forearm of healthy volunteers. Arms were extended/flexed for the range of motion of three different regimes: 1) horizontal adduction/abduction 2) flexion/extension 3) vertical abduction. Data were streamed and recorded revealing the signal "pattern" of movement in three separate axes. Additional signal processing and filtering afforded the ability to visualize these motions in each plane of the body; and the 3-dimensional motion envelope of the arm.

Results: Each of the three motion regimes studied had a distinct pattern - with identifiable qualitative and quantitative differences. Integration of all three movement regimes allowed construction of a "motion envelope," defining and quantifying motion (range and shape - including the outer perimeter of the extreme of motion - i.e. the envelope) of the upper extremity. The linear and rotational motion results from multiple arm motions match measurements taken with videography and benchtop goniometer.

Conclusions: A conformal, stretchable electronic motion sensor effectively captures limb motion in multiple degrees of freedom, allowing generation of characteristic signatures which may be readily recorded, stored, and analyzed. Wearable conformal skin adherent sensor patchs allow on-body, mobile, personalized determination of motion and flexibility parameters. These sensors allow motion assessment while mobile, free of a fixed laboratory environment, with utility in the field, home, or hospital. These sensors and mode of analysis hold promise for providing digital "motion biomarkers" of health and disease.

Keywords: Accelerometers; Biomechanics; Biomedical measurement; Biometrics; Biosensors; Biotechnology; Engineering in medicine and biology; Gyroscopes; Motion analysis; Wearable sensors.

Fig. 1

Schematic of Wearable BioStampRC. (a) Top view of BioStampRC (b) Bottom view of BioStampRC (c) Angled side view of BioStampRC on wireless charging platform. Images provided by MC10, Inc.

Fig. 2

Characterization and Accuracy of BioStampRC. (a) Tri-axial orientation of the BioStampRC during acceleration and gyroscope recordings: x-plane (blue), y-plane (red), and zplane (green). BioStampRC image provided by MC10 Inc. (b) Top view of BioStampRC on distal end of goniometer on flat surface at starting position (left) and after 180 ° movement about BioStampRC z-axis. (c) BioStampRC angular position about z-axis after 180 ° movement on goniometer. Values shown as average degrees ± standard deviation (n = 3). (d) Top view of BioStampRC on distal volar surface of arm while on flat surface at starting position (left) and after 110 ° movement in the x-z plane, about y-axis. (e) Displacement output from BioStampRC accelerometer measurements after arm rotation at decreasing velocities (left to right). (f) Accuracy of X and Z displacement measurements at different rotational speeds. Values shown as average meters ± standard deviation (n ≥ 8)

Fig. 3

BioStampRC and Body Orientation during Motion. (a) Three planes of the body in anatomical position: frontal plane (blue), transverse plane (green), and sagittal plane (red). (b) Placement of BioStampRC on volar surface of the forearm. (c) Top view of horizontal adduction and abduction of arm with subject in supine position. Motion is performed with straight arm in the transverse plane and about the BioStampRC y-axis (d) Side view of flexion and extension of arm with subject sitting straight. Motion is performed with straight arm in the sagittal plane and about the BioStampRC z-axis. (e) Front view of vertical abduction of arm with subject sitting straight. Motion is performed with straight arm in the frontal plane and about the BioStampRC z-axis

Fig. 4

BioStampRC triaxial Motion Data. Triaxial acceleration (left) and angular velocity (right) for (a) horizontal abduction and adduction of the arm, (b) flexion and extension of the arm, and (c) vertical abduction of the arm

Fig. 5

Video versus BioStampRC Data. Screenshot from motion video (left) and corresponding BioStampRC angular position (right) for (a) horizontal adduction of the arm about BioStampRC y-axis, (b) horizontal abduction of the arm about BioStampRC y-axis, (c) flexion of the arm about BioStampRC z-axis, (d) extension of the arm about BioStampRC z-axis, and (e) vertical abduction of the arm about BioStampRC z-axis. Yellow angles represent starting position of arm to the stopping position for each motion

Fig. 6

Three-Dimensional Representation of Healthy and Reduced Shoulder Range of Motion. Extent of range of movement for healthy subject in the transverse plane (a), sagittal plane (b), frontal plane (c) and the corresponding 3-dimensional digital representation (d). Extent of range of movement for subject exhibiting reduced motion in transverse plane (e), sagittal plane (f), frontal plane (g) and corresponding 3-dimensional digital representation (h)

Fig. 7

Three-Dimensional Motion Envelope of Human Shoulder. BioStampRC tri-axial arm displacement over time during gradual (a), leveled (b), and random (c) motion of the arm. Calculated three-dimensional displacement of arm during gradual (d), leveled (e), and random (f) motion of the arm