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Wearable Sensor Data-Driven Walkability Assessment for Elderly People
Active living improves the lives and social networks of the elderly. In terms of active living, walkability is an essential element in the daily life of the elderly. To support active living, it is important to create an age-friendly environment. Considering that the elderly carry out a large part of their activities by walking, a good walkable environment is one of the most important elements of an age-friendly environment. Existing studies have involved surveys of experts, audit tools, and questionnaires. However, despite their merits, current methods of measuring walkability remain limited as they do not include the actual walking activity of the elderly. Therefore, the purpose of this study is to investigate the possibility of using a wearable sensor to measure the walking of the elderly quantitatively, and to compare different walking environments based on data collected from their actual walking. To accomplish this, experiments were conducted in four types of environments with 30 elderly subjects. During the experiments, the subjects were asked to attach a smartphone that includes an inertial measurement unit (IMU). The IMU sensor collected the body movement using tri-axial accelerations. The collected data were used to calculate walkability by investigating how constant a subject’s walking pattern is. The consistency of pattern can be regarded as gait stability that can be quantitatively measured via the maximum Lyapunov exponent (MaxLE—a metric used for measuring the stability of human body during locomotion. As a result of the experiment, it was found that the stability of walking of elderly people differs according to the walking environment, which means that by investigating the stability the current conditions of a specific walking environment can be inferred. This result helps improve the active life of the elderly by providing opportunities for continuous diagnosis of the walking environment.
Wearable Sensor Data-Driven Walkability Assessment for Elderly People
Active living improves the lives and social networks of the elderly. In terms of active living, walkability is an essential element in the daily life of the elderly. To support active living, it is important to create an age-friendly environment. Considering that the elderly carry out a large part of their activities by walking, a good walkable environment is one of the most important elements of an age-friendly environment. Existing studies have involved surveys of experts, audit tools, and questionnaires. However, despite their merits, current methods of measuring walkability remain limited as they do not include the actual walking activity of the elderly. Therefore, the purpose of this study is to investigate the possibility of using a wearable sensor to measure the walking of the elderly quantitatively, and to compare different walking environments based on data collected from their actual walking. To accomplish this, experiments were conducted in four types of environments with 30 elderly subjects. During the experiments, the subjects were asked to attach a smartphone that includes an inertial measurement unit (IMU). The IMU sensor collected the body movement using tri-axial accelerations. The collected data were used to calculate walkability by investigating how constant a subject’s walking pattern is. The consistency of pattern can be regarded as gait stability that can be quantitatively measured via the maximum Lyapunov exponent (MaxLE—a metric used for measuring the stability of human body during locomotion. As a result of the experiment, it was found that the stability of walking of elderly people differs according to the walking environment, which means that by investigating the stability the current conditions of a specific walking environment can be inferred. This result helps improve the active life of the elderly by providing opportunities for continuous diagnosis of the walking environment.
Wearable Sensor Data-Driven Walkability Assessment for Elderly People
Hyunsoo Kim (author)
2020
Article (Journal)
Electronic Resource
Unknown
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