ArticleEntropy (Basel, Switzerland)2020
Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 116 citations in OpenAlex.
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- Exploratory development of human-machine interaction strategies for post-stroke upper-limb rehabilitation.Journal of neuroengineering and rehabilitation · 2025Article
- Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace.Sensors (Basel, Switzerland) · 2025Review
- Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- Wireless body area sensor networks based human activity recognition using deep learning.Scientific reports · 2024Article
- A robust multimodal detection system: physical exercise monitoring in long-term care environments.Frontiers in bioengineering and biotechnology · 2024Article
- A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity.Sensors (Basel, Switzerland) · 2023Article
- Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation.Sensors (Basel, Switzerland) · 2023Article
- Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition.Scientific reports · 2023Article
- Hand Exoskeleton Design and Human-Machine Interaction Strategies for Rehabilitation.Bioengineering (Basel, Switzerland) · 2022Article
- Activity-Aware Vital SignMonitoring Based on a Multi-Agent Architecture.Sensors (Basel, Switzerland) · 2021Article
- Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier.Entropy (Basel, Switzerland) · 2021Article
- Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders.Sensors (Basel, Switzerland) · 2021Article
- A Low-Cost, Autonomous Gait Detection and Estimation System for Analyzing Gait Impairments in Mice.Journal of healthcare engineering · 2021Article
- Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning.Frontiers in neurorobotics · 2021Article
- Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors.Entropy (Basel, Switzerland) · 2020Article
- Multimodal healthcare system for human activity recognition using multiple features and advanced ensemble classifier.Digital healthArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky-Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the "leave-one-out" cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man-machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.
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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.