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Title:
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FALL DETECTION METHOD USING SINGLE AXIS ACCELERATION AND ANGUALAR VELOCITY |
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Author(s):
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Daniel Badran, Kabalan Chaccour, Amir Hajjam El Hassani and Emmanuel Andres |
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ISBN:
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978-989-8533-65-4 |
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Editors:
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Mário Macedo |
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Year:
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2017 |
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Edition:
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Single |
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Keywords:
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Ageing; Fall detection; Body posture; Wearable systems; Inertial sensors |
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Type:
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Full Paper |
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First Page:
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87 |
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Last Page:
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94 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Falls of elderly people are a major public health issue. For the victims and their surroundings, the resulting health expenses can be a heavy burden, largely because of the long recovery periods and the potential of co-morbidities that arise. Research efforts in fall detection have produced a wide range of solutions. These are classified as wearable and non-wearable systems. Fall detection algorithms running on these systems are being tested by young volunteers inside lab environment taking out the impact of real-life scenarios and real elderly people. Unlike other algorithms that use three-axial acceleration and angular velocity to detect a fall, our algorithm uses one single axis. The algorithm was validated with 8 elderly aging above 65 years and with either a minor walking disorder or coping from either fear of fall or previous fall injuries. Results showed 99% sensitivity and 94.8% specificity. The system has a registered accuracy of 97.5%. |
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