A systems analysis and technology roadmap for fall mitigation systems for the elderly

Falls and fall related injuries in the elderly (aged 65 and older) are a major health challenge - both to the affected individual and to the public health system. Approximately 28-35% of the elderly fall each year and falls lead to 20-30% of mild to severe injuries, and are underlying cause of 10-15% of all emergency room (ER) visits. Falls cause 90% of the hip fractures in the elderly and also result in medical complications and high morbidity if the person does not receive prompt medical attention. A fall mitigation system (FMS) is either a wearable or ambient system that detects falls, reduces fall related injuries and issues emergency alerts to prevent the long-lie. Current FMS have poor user adoption and are not as effective in preventing the long-lie. This thesis uses a systems approach to analyze architectures for a fall mitigation system architecture that can detect falls, reduce injury and issue emergency alerts to reliably prevent the long-lie in independent elders. A National Health Interview Survey data was analyzed to understand the causes for falls, types of fall related injuries and common fall locations for community dwelling elders. A concept of operations was defined based on these findings and a user survey was conducted to understand the needs of community dwelling elders and the results were analyzed to prioritize system requirements for a fall mitigation system (FMS). An FMS was decomposed into six level 2 functions and the various form choices for each of these functions were analyzed and rated for performance, power consumption and cost. Five different fall mitigation system architectures were analyzed and the Distributed-Hybrid architecture had the highest performance while the Integrated-Wearable architecture had the lowest power consumption. Future technology trends in robotics, AI, neuromorphic computing and energy harvesting were studied to create a long-term strategic roadmap for fall mitigation systems. Neuromorphic architectures for computing and sensing offer the biggest performance per unit power unlock for fall mitigation systems.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Enti Ranga Reddy, Vikas Reddy.

Advisor dc:contributor.advisor

• Bruce G. Cameron.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132818

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132818