ArticleJournal of neuroengineering and rehabilitation2021
A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls.
Article in Journal of neuroengineering and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Research of Fall Detection and Fall Prevention Technologies: A Review.Sensors (Basel, Switzerland) · 2026Review
- Context-Aware Alerting in Elderly Care Facilities: A Hybrid Framework Integrating LLM Reasoning with Rule-Based Logic.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Real-World Fall Detection Using Commodity Devices: A Systematic Study.Sensors (Basel, Switzerland) · 2025Article
- Multimodal Sensing-Enabled Large Language Models for Automated Emotional Regulation: A Review of Current Technologies, Opportunities, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings.Sensors (Basel, Switzerland) · 2024Article
- Comparison of professional and everyday wearable technology at different body positions in terms of recording gait perturbations.PLOS digital health · 2024Article
- Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.Sensors (Basel, Switzerland) · 2022Article
- A survey for characterizing details of fall events experienced by lower limb prosthesis users.PloS one · 2022Article
- LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundFalls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification.
methodsThe system uses the smartphone's accelerometer and gyroscope to monitor the participants' motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller's location and activity before the fall.
resultsIn total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles.
conclusionsThe system's performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.
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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.