Robotic Assistive Devices for Real-Time Fall Prevention: Advancing Active Ageing
A special issue of Sensors (ISSN 1424-8220).
Deadline for manuscript submissions: 28 February 2025 | Viewed by 8
Special Issue Editors
Interests: human motion; human locomotion; human–robot interactions and collaboration; medical devices; neuro-rehabilitation of patients suffering from motor problems by means of bio-inspired robotics and neuroscience technologies
Special Issues, Collections and Topics in MDPI journals
2. LABBELS—Associate Laboratory, 4710-057 Braga, Portugal
3. LABBELS—Associate Laboratory, 4800-058 Guimarães, Portugal
Interests: interoperability; user experience; usability; mechanics; biomedical engineering; gait analysis; sensors; fall prediction and prevention; robotics; artifcial intelligence; product design and development
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Falls constitute a major public health issue, accounting for 40% of all accidental deaths and emerging as the sixth leading cause of mortality among the elderly. Annually, these incidents result in approximately 684,000 fatal falls and an estimated 37.3 million non-fatal falls. It is noteworthy that 42% of individuals aged 65 and older experience at least one fall per year, typically due to slipping or tripping. Moreover, falls stand out as a primary cause of injury-related disability, placing them among the top five causes of years lived with disability. This escalating concern reflects the pressing need for innovative solutions to mitigate fall risks and promote healthy aging among seniors.
Existing technological systems primarily focus on detecting falls and issuing alerts through applications but overlook the critical aspect of predicting and preventing falls. This creates a significant gap in the development of early warning systems and timely preventive measures to mitigate fall risks. Notably, no solution has yet addressed this combination within the context of continuous monitoring during daily activities. To foster active aging, it is imperative to encourage walking and enhance seniors' mobility, thus promoting independence. A diverse range of robotic devices, including cane-type robots, smart walkers, and active orthoses, provide real-time fall prevention and support. Consequently, interdisciplinary research is crucial for advancing innovative fall prevention technologies.
This special issue aims to showcase cutting-edge research in robotic assistive devices capable of monitoring users and providing proactive support during gait, with a primary focus on real-time fall prevention. By leveraging interdisciplinary collaboration, we seek to address the gap in early warning systems and timely preventive actions to mitigate falls during activities of daily living. The Special Issue welcomes submissions describing the application, technologies, and/or validation of innovative approaches, in areas including:
- Real-time multifactorial data collection for fall risk assessment.
- Continuous fall risk assessment during gait and other activities.
- Natural and synergetic human-robot interaction for intuitive gait support.
- Technological methods for risk and fall prediction.
- Intuitive and personalized gait support through innovative technology.
- Proactive fall prevention using biofeedback mechanisms.
- Reliability and validity of risk and fall predictions.
- Cost-effectiveness analysis of technologies for fall prevention.
Dr. Cristina P. Santos
Dr. Nuno Ribeiro
Guest Editors
Manuscript Submission Information
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Keywords
- robotic assistive devices
- fall risk assessment
- gait
- human-robot interaction
- fall prediction
