Abstract
Falls and falls related injuries are the major causes of non-fatal injuries in older adults. With recent advances in mathematics, science and technology, many scientists and engineers are devoting their efforts to prevent falls or to diminish the negative health outcomes after falls. In this chapter, we briefly review major engineering approaches to recover or augment the human gait function pre- and post-falls. Given the proliferation of wearable sensors and the availability of computational resources in the last decade, we focused on the role of wearable sensors to monitor gait instabilities and potentially prevent falls. We reviewed the general framework for gait monitoring using wearables and its utility in real-life settings such as homes or retirement communities. In the last part of the chapter, we focused on recent contributions that have proposed wearable sensors for gait monitoring and fall inferences.
Original language | English |
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Title of host publication | Falls and Cognition in Older Persons |
Subtitle of host publication | Fundamentals, Assessment and Therapeutic Options |
Editors | Manuel Montero-Odasso, Richard Camicioli |
Publisher | Springer |
Pages | 401-426 |
Number of pages | 26 |
ISBN (Electronic) | 9783030242336 |
ISBN (Print) | 9783030242329 |
DOIs | |
Publication status | Published - Oct 2019 |
Keywords
- Wearables
- Data science
- Machine learning
- Artificial intelligence
- Accelerometer
- Falls
- Gait
- Engineering
- Physical therapy
- Geriatrics