Muscle fatigue prediction
- A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue
- Exploring State-of-the-Art Machine Learning Methods for Quantifying Exercise-induced Muscle Fatigue
- Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey
- Assessment of Muscles Fatigue Based on Surface EMG Signals Using Machine Learning and Statistical Approaches: A Review
- Physical fatigue detection through EMG wearables and subjective user reports: a machine learning approach towards adaptive rehabilitation
- Data-driven predictive maintenance and time-series applications
- Assessing Fatigue with Multimodal Wearable Sensors and Machine Learning
- Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification
- Nutrition, Neurotransmitters and Central Nervous System Fatigue
Data collection with sensors
- Benchmarking of the BITalino biomedical toolkit against an established gold standard
- Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation
Art X Science
- Know thy Flesh: What Multi-disciplinary Contemporary Art Teaches Us about Building Body Knowledge
- The sonfication of EMG data
- Art, Science, and the Politics of Knowledge
- Challenging the “Data Body” in New Media Art, 1990s–Present
- Materializing Datafied Body Doubles: Insulin Pumps, Blood Glucose Testing, and the Production of Usable Bodies
Self-tracking
- Digital health for chronic disease management: An exploratory method to investigating technology adoption potential
- Our metrics, ourselves: A hundred years of self- tracking from the weight scale to the wrist wearable device
- Opinion leader empowered patients about the era of digital health: a qualitative study
- Data for life: Wearable technology and the design of self-care