Role of body-worn movement monitor technology for balance and gait rehabilitation

Abstract

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.

Figure 1.

(A) Photo of therapist (F.H.) with a patient wearing movement monitors on his sternum, belt, ankles, and wrists attached with elastic bands. A close-up of the APDM Opal monitors (APDM Inc, Portland, Oregon) is shown in inset below. (B) Representative raw data of angular velocity signals detected from the lower leg and the mediolateral (ML) acceleration signal from the trunk during the Instrumented Stand and Walk Test (ISAW), divided into 4 phases: quiet standing, step initiation, gait, and turning. (C) Group mean and standard errors of clinical and instrumented tests to distinguish mobility in healthy control participants from those with mild multiple sclerosis (MS) (n=31) or untreated Parkinson disease (PD) (n=12). The clinical 25-ft walk time (T25FW) and the Timed “Up & Go” Test (TUG) could not distinguish between MS or PD and age-matched control participants, respectively. However, significant differences (*P<.05) were found between groups for several objective, instrumented measures. Data adapted from published studies., ROM=range of motion.

Figure 2.

Gait stride time from movement monitors on the ankles during a 2-minute walk at a comfortable speed is more variable in a young athlete after a sports concussion compared with an age-matched control athlete.

Figure 3.

Reduction of postural sway with audio-biofeedback (ABF) from a movement monitor on the belt during stance on (A) a firm surface and (B) a foam surface with eyes closed in a patient with a mild traumatic brain injury (TBI). AP Acc=anteroposterior acceleration, ML Acc=mediolateral acceleration.

Figure 4.

Continuous measures of turning velocity over a week (mean turn average velocity) is more sensitive than clinical gait speed (time to walk 9 ft) in predicting fallers in a sample of 19 elderly fallers (≥1 fall in the last year) and 16 elderly nonfallers. On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, and the outliers are plotted as +. *P<.05.