Abstract
This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications.
Highlights
Gait dynamics reflect one’s mobility which can be affected by physical impairment, age progress and changes in health status
This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition
The autocorrelation sequences computed from the healthy young subjects exhibit a more smooth and monotonic pattern, while the counterparts obtained from the Parkinson’s disease (PD) patients contain visible fluctuations and are less regular
Summary
Gait dynamics reflect one’s mobility which can be affected by physical impairment, age progress and changes in health status. Ambulatory gait parameters can be important measures to assess functional ability, balance control and to predict fall risk. Individuals with degenerative mobility, e.g., Parkinson’s disease (PD) patients or older adults usually have gait disorders such as reduced walking speeds with increased cadences, reduced step/stride lengths, and increased inter-stride variability [1]. PD patients of advanced stage might have encountered episodic gait disturbances, like festinating or even freezing of gaits (FOG) that could lead to falling and adverse health outcomes [2,3]. Regularity, rhythm and symmetry are important gait cycle parameters that can be apparently altered in walking patterns among people of varied mobility [2,3,4]. The monitoring of the above gait cycle parameters can be beneficial to assess the mobility and risk of occurrence of episodic gait disturbances
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