Abstract
Clinical gait analysis provides an evaluation tool that allows clinicians to characterize person’s locomotion at a particular time. There are currently specialized systems to detect gait events and compute spatio-temporal parameters of human gait, which are accurate and redundant. These systems are expensive and are limited to controlled settings with gait evaluations widely spaced in terms of time. As alternative, a proposal for long-term gait monitoring in Assisted Living Environments based on an infrastructure of wireless inertial sensors is presented. Specifically, heel-strike events will be identified in multiple elders in a rest home and throughout the day. A small wearable device composed of a single inertial measurement unit will be placed at the back of each elder, on the thoracic zone, capturing trunk accelerations and orientations which will enable the demarcation of heel-strike events and the computation of temporal gait parameters. This proposal attempts to contribute to the development of a less intrusive and reachable alternative for long-term gait monitoring of multiple residents, which has been poorly investigated.
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Notes
- 1.
Inter-Integrated Circuit data serial bus.
- 2.
6-Degrees of Freedom IMU (3-axis accelerometer and 3-axis gyroscope).
- 3.
MQTT (Message Queuing Telemetry Transport). It is a lightweight transport protocol based on publication/subscription policies to defined messages, known as topics.
- 4.
Gait parameter estimation in clinical settings generally uses straight paths because turns or changes in the gait trajectory distort the measurements.
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Acknowledgements
This work is supported by the FRASE MINECO project (TIN2013-47152-C3-1-R) and the Plan Propio de Investigación from Castilla-La Mancha University.
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González, I., Fontecha, J., Hervás, R., Naranjo, M., Bravo, J. (2016). A Proposal for Long-Term Gait Monitoring in Assisted Living Environments Based on an Inertial Sensor Infrastructure. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_31
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