ArticleHealthcare (Basel, Switzerland)2021
A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A system for monitoring the functional status of older adults in daily life.Scientific reports · 2023Trial
- A deep learning framework for gait-based frailty classification using inertial measurement units.PloS one · 2026Article
- AI-Assisted Dynamic Postural Control Screening to Improve Functional Mobility in Older Adult Populations: Quasi-Experimental Study.JMIR aging · 2025Article
- Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Frailty identification using a sensor-based upper-extremity function test: a deep learning approach.Scientific reports · 2025Article
- Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements.Bioengineering (Basel, Switzerland) · 2024Article
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision).Physical therapy · 2024Article
- Classification of Parkinson's disease stages with a two-stage deep neural network.Frontiers in aging neuroscience · 2023Article
- Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach.IEEE journal of biomedical and health informatics · 2022Article
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This research introduces an approach for predicting the score of the Timed Up & Go test and Short-Physical-Performance-Battery assessment using IMU data and deep neural networks. The approach is validated on real-world data of a cohort of 20 frail or (pre-) frail older adults of an average of 84.7 years. The deep neural networks achieve an accuracy of about 95% for both tests for participants known by the network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.