Triggering Gait Assistance: Motion Intention and Early Fall Signs Detection to Foster Balance Awareness via Biofeedback in a Cane-Type Robot
Description: Seniors and patients with gait-related diseases face limitations in daily living and present high fall risk.Current technologies address post-fall actions,neglecting prevention strategies.There is a notable gap in research focusing on Early Fall Signs(EFS)detection for fall prevention and postural control due to demanding and intrincate challenges involved.This proposal extends ongoing work to enhance an existing cane-type robot by incorporating gait monitoring capabilities and proactive user warning.This work will include:i)data collection from provoked falls and abnormal gait situations;ii)development of bio-inspired artificial intelligence(AI)-based gait estimation algorithms;and iii) gait correction context-dependent biofeedback systems(BSs).Prototype(Technology Readiness Level(TRL)5)is functional in environments resembling final conditions of use,following validation in a laboratory setting with elderly subjects from institutional settings to improve usability,security,and performance considering key performance metrics.A multidisciplinar team ensures successful PhD outcomes with breakthrough technology for human healthcare and assistance towards preventive and at-home medicine,granting independent and active/social living,economic growth,and industrial innovation(3rd,8th,9th Sustainable Development Goals(SDGs)).
Amount: €38,280
Funding type: grant
Funder scheme: FCT
Start date: 2025-01-01
End date: 2028-12-31
Investigator: JOSÉ LUÍS FERNANDES PEREIRA
Funded outputs count: 0
Funder award ID: 2024.02459.BD
OpenAlex ID: G4121076203