ArticleSensors (Basel, Switzerland)2016
Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets.
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 18 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Technological applications to enhance independence in daily activities for older adults: a systematic review.Frontiers in public health · 2024Pooled it
- Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2020Pooled it
- AI-Driven Activity of Daily Living Monitoring: A Comprehensive Review of Ambient, Wearable, and Fusion-Based Sensing Technologies.Annals of biomedical engineering · 2026Review
- Simplifying Prediction of Intended Grasp Type: Accelerometry Performs Comparably to Combined EMG-Accelerometry in Individuals With and Without Amputation.Sensors (Basel, Switzerland) · 2025Article
- A Review on Assisted Living Using Wearable Devices.Sensors (Basel, Switzerland) · 2024Review
- Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study.JMIR human factors · 2023Article
- The VISTA datasets, a combination of inertial sensors and depth cameras data for activity recognition.Scientific data · 2022Article
- Review
- Article
- Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.PloS one · 2021Article
- An Energy-Based Method for Orientation Correction of EMG Bracelet Sensors in Hand Gesture Recognition Systems.Sensors (Basel, Switzerland) · 2020Article
- A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms.Frontiers in psychiatry · 2020Article
- Article
- StraightenUp+: Monitoring of Posture during Daily Activities for Older Persons Using Wearable Sensors.Sensors (Basel, Switzerland) · 2018Article
- Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons.Sensors (Basel, Switzerland) · 2017Article
- Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2017Article
- Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.Sensors (Basel, Switzerland) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios.
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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.