Fall Detection and Prevention System for the Elderly

There is an urgent need for developing monitoring systems that can detect near-falls and alert persons who suffer from subsequently falls. Therefore, investigators propose a project to study the clinical application of a fall detection and prevention system for reducing falls in the elderly.

The main objectives of the project are to develop the fall detection and prevention system and to investigate its effectiveness in preventing falls for the elderly. Eighty elderly will be recruited and randomly allocated into either the experimental or sham groups (n=40 for each group). Subjects in the experimental group will wear the fall detection and prevention system on the lower back. The system records near-fall and fall events; meanwhile, it alarms subjects while detecting near-fall events and alarms caregivers while detecting fall events. In the sham group, subjects wear a sham system with record but no alert function. Both groups will receive the same treadmill walking training for 30 minutes per session, 3 sessions per week for 6 weeks. The outcome measures including falls, balance function, gait ability, physical activity, and quality of life will be assessed before intervention, after intervention, and at 6-months follow-up.

Fall-induced physiological and psychological consequences limit daily activities and reduce quality of life in the elderly. The proposed study innovates a system that not only detects but also alarms users to prevent falls. Moreover, quantitative measurement of such fall detection and prevention system will be investigated for the elderly in this project. This study provides practical and efficacy solutions to prevent falls for home and community settings.

Fall prevention is the most important issue for the elderly. Due to the low cost and convenience of wearable devices, numerous techniques have been developed to detect falls in clinical environment. However, such effects with large populations have not been established for the elderly. Another, existing systems mainly focus on detecting a fall with little emphasis on fall prediction and prevention. There is an urgent need for developing monitoring systems that can detect near-falls and alert persons who suffer from subsequently falls. Therefore, investigators propose a project to study the clinical application of a fall detection and prevention system for reducing falls in the elderly.

The main objectives of the project are to develop the fall detection and prevention system and to investigate its effectiveness in preventing falls for the elderly. Eighty elderly will be recruited and randomly allocated into either the experimental or sham groups (n=40 for each group). Subjects in the experimental group will wear the fall detection and prevention system on the lower back. The system records near-fall and fall events; meanwhile, it alarms subjects while detecting near-fall events and alarms caregivers while detecting fall events. In the sham group, subjects wear a sham system with record but no alert function. Both groups will receive the same treadmill walking training for 30 minutes per session, 3 sessions per week for 6 weeks. The outcome measures including falls, balance function, gait ability, physical activity, and quality of life will be assessed before intervention, after intervention, and at 6-months follow-up.

Fall-induced physiological and psychological consequences limit daily activities and reduce quality of life in the elderly. The proposed study innovates a system that not only detects but also alarms users to prevent falls. Moreover, quantitative measurement of such fall detection and prevention system will be investigated for the elderly in this project. This study provides practical and efficacy solutions to prevent falls for home and community settings.

Eligibility Criteria:
Inclusion Criteria:

* Age ≥ 65 years
* Able to walk independently for at least 5 m with or without the use of stick, quad stick or AFO
* With a history or fear of falls
* Able to follow the commands

Exclusion Criteria:

* With a severe cardiovascular, respiratory, musculoskeletal or neurological disorder