Abstract
In this work, we develop a novel gait phase detection algorithm based on a hidden Markov model, which uses data from foot-mounted single-axis gyroscopes as input. We explore whether the proposed gait detection algorithm can generate equivalent results as a reference signal provided by force sensitive resistors (FSRs) for typically developing children (TD) and children with hemiplegia (HC). We find that the algorithm faithfully reproduces reference results in terms of high values of sensitivity and specificity with respect to FSR signals. In addition, the algorithm distinguishes between TD and HC and is able to assess the level of gait ability in patients. Finally, we show that the algorithm can be adapted to enable real-time processing with high accuracy. Due to the small, inexpensive nature of gyroscopes utilized in this study and the ease of implementation of the developed algorithm, this work finds application in the on-going development of active orthoses designed for therapy and locomotion in children with gait pathologies.
Highlights
Disorders of gait affect an estimated 1.1 million children in the United States as of 2007 [1] and may originate from different somatosensory conditions [2]
We find significant differences depending on health status in ST and heel off (HO) for seven and six out of eight tasks, respectively, which reflects toe walking exhibited by many patients with hemiplegia
We find the minimum mean values for specificity and sensitivity to be very large for all conditions
Summary
Disorders of gait affect an estimated 1.1 million children in the United States as of 2007 [1] and may originate from different somatosensory conditions [2]. Therapies, and rehabilitation strategies for these disorders are impacted by the complex problem of recognizing the repeated features or ‘‘phases’’ of the gait cycle [3]. Therapies which rely on such information include functional electrical stimulation (FES) [4,5], active orthoses [6,7,8,9], and behavioural strategies [10]. While current FES therapies actively assist walking, they rely on patients’ manual stimulation for even simple locomotory tasks [11]. The use of gait identification in active orthoses is expected to permit such therapies to operate synchronously and synergistically with patients [8]. Behavioural training based on adherence enhancing strategies needs accurate feedback to obtain gait symmetry [10]
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