Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention.
The proliferation of miniaturized electronics has fueled a shift toward wearable sensors and feedback devices for the mass population. Quantified self and other similar movements involving wearable systems have gained recent interest. However, it is unclear what the clinical impact of these enabling technologies is on human gait. The purpose of this review is to assess clinical applications of wearable sensing and feedback for human gait and to identify areas of future research. Four electronic databases were searched to find articles employing wearable sensing or feedback for movements of the foot, ankle, shank, thigh, hip, pelvis, and trunk during gait. We retrieved 76 articles that met the inclusion criteria and identified four common clinical applications: (1) identifying movement disorders, (2) assessing surgical outcomes, (3) improving walking stability, and (4) reducing joint loading. Characteristics of knee and trunk motion were the most frequent gait parameters for both wearable sensing and wearable feedback. Most articles performed testing on healthy subjects, and the most prevalent patient populations were osteoarthritis, vestibular loss, Parkinson's disease, and post-stroke hemiplegia. The most widely used wearable sensors were inertial measurement units (accelerometer and gyroscope packaged together) and goniometers. Haptic (touch) and auditory were the most common feedback sensations. This review highlights the current state of the literature and demonstrates substantial potential clinical benefits of wearable sensing and feedback. Future research should focus on wearable sensing and feedback in patient populations, in natural human environments outside the laboratory such as at home or work, and on continuous, long-term monitoring and intervention.
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: pshull@sjtu.edu.cn.
Pete B
PB
Shull
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Wisit
W
Jirattigalachote
Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.
Michael A
MA
Hunt
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Mark R
MR
Cutkosky
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Scott L
SL
Delp
D000328:Adult; D000842:Ankle / Q000502:physiology; D001676:Biofeedback, Psychology / Q000295:instrumentation*; D003020:Clothing; D006259:Craniocerebral Trauma / Q000503:physiopathology; D004867:Equipment Design; D056228:Feedback, Sensory / Q000502:physiology*; D005528:Foot / Q000502:physiology; D005684:Gait / Q000502:physiology*; D006429:Hemiplegia / Q000503:physiopathology; D006801:Humans; D007717:Knee / Q000502:physiology; D007866:Leg / Q000502:physiology; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009068:Movement / Q000502:physiology*; D009069:Movement Disorders / Q000503:physiopathology*; D010003:Osteoarthritis / Q000503:physiopathology; D010300:Parkinson Disease / Q000503:physiopathology; D012016:Reference Values; D058998:Remote Sensing Technology / Q000295:instrumentation*; D014159:Transducers; D016138:Walking / Q000502:physiology; D016474:Weight-Bearing / Q000502:physiology; D059015:Wireless Technology / Q000295:instrumentation
Biofeedback; Gait retraining; Haptic; Motion analysis; Real-time feedback