ArticleSensors (Basel, Switzerland)2016
An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Fall Detection in Elderly People: A Systematic Review of Ambient Assisted Living and Smart Home-Related Technology Performance.Sensors (Basel, Switzerland) · 2025Pooled it
- Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review.Clinical interventions in aging · 2020Pooled it
- Article
- Wearable Fall Detection System with Real-Time Localization and Notification Capabilities.Sensors (Basel, Switzerland) · 2025Article
- Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions.Sensors (Basel, Switzerland) · 2024Article
- Wrist-Based Fall Detection: Towards Generalization across Datasets.Sensors (Basel, Switzerland) · 2024Article
- A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups.Sensors (Basel, Switzerland) · 2024Article
- Exploiting the features of deep residual network with SVM classifier for human posture recognition.PloS one · 2024Article
- Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning Technique.Sensors (Basel, Switzerland) · 2023Article
- Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable.Wearable technologies · 2023Article
- Article
- Identification of the cause of fall during the pre-impact fall period.Journal of physical therapy science · 2022Article
- Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress.Computational intelligence and neuroscience · 2022Review
- Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.Sensors (Basel, Switzerland) · 2021Article
- The Design and Simulation of a 16-Sensors Plantar Pressure Insole Layout for Different Applications: From Sports to Clinics, a Pilot Study.Sensors (Basel, Switzerland) · 2021Article
- Dynamic Visual Stimulations Produced in a Controlled Virtual Reality Environment Reveals Long-Lasting Postural Deficits in Children With Mild Traumatic Brain Injury.Frontiers in neurology · 2021Article
- A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.Frontiers in aging neuroscience · 2021Article
- Subtask Segmentation of Timed Up and Go Test for Mobility Assessment of Perioperative Total Knee Arthroplasty.Sensors (Basel, Switzerland) · 2020Article
- Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.IEEE sensors journal · 2020Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer's movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today's wearable applications.
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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.