ArticleBiomedical engineering online2018
Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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, 87 citations in OpenAlex.
- Microcontroller Implementation of LSTM Neural Networks for Dynamic Hand Gesture Recognition.Sensors (Basel, Switzerland) · 2025Article
- MOT: A Low-Latency, Multichannel Wireless Surface Electromyography Acquisition System Based on the AD8232 Front-End.Sensors (Basel, Switzerland) · 2025Article
- A Review on Assisted Living Using Wearable Devices.Sensors (Basel, Switzerland) · 2024Review
- Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of wearable sensors in upper extremity MSK conditions: a scoping review.Journal of neuroengineering and rehabilitation · 2023Article
- Microfluidic Wearable Devices for Sports Applications.Micromachines · 2023Review
- Erector Spinae Muscle Activation During Forward Movement in Individuals With or Without Chronic Lower Back Pain: A Systematic Review and Meta-analysis.Archives of rehabilitation research and clinical translation · 2023Review
- Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing.Sensors (Basel, Switzerland) · 2023Article
- Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength.Sensors (Basel, Switzerland) · 2023Article
- Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review.Journal of medical Internet research · 2023Article
- Monitoring Breathing and Heart Rate Using Episodic Broadcast Data Transmission.Sensors (Basel, Switzerland) · 2022Article
- An Alternative Athlete Monitoring System Using Cost-Effective Inertial Sensing Instrumentation.Journal of electrical engineering & technology · 2022Article
- Biopotential Signal Monitoring Systems in Rehabilitation: A Review.Sensors (Basel, Switzerland) · 2021Review
- An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications.Biosensors · 2021Article
- Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors.Sensors (Basel, Switzerland) · 2021Article
- Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies.Sensors (Basel, Switzerland) · 2020Review
- A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal.Sensors (Basel, Switzerland) · 2019Article
- Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization.Sensors (Basel, Switzerland) · 2018Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare of elderly people. The activity monitoring is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications.
resultsThe proposed system consists of several ultralight wireless sensing nodes that are able to acquire, process and efficiently transmit the motion-related (biological and accelerometer) body signals to one or more base stations through a 2.4 GHz radio link using an ad-hoc communication protocol designed on top of the IEEE 802.15.4 physical layer. A user interface software for viewing, recording, and analysing the data was implemented on a control personal computer that is connected through a USB link to the base stations. To demonstrate the capability of the system of detecting the user's activity, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. The system was tested on four different exercises performed by three people, the automatic classifier achieved an overall accuracy of 85.7% combining the features extracted from acceleration and sEMG signals.
conclusionsA low cost wireless system for the acquisition of sEMG and accelerometer signals has been presented for healthcare and fitness applications. The system consists of wearable sensing nodes that wirelessly transmit the biological and accelerometer signals to one or more base stations. The signals so acquired will be combined and processed in order to detect, monitor and recognize human activities.
Indexed as
Identifiers
What OpenQuestion holds
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.