A Preliminary Experimental Outline to Train Machine Learning Models for the Unobtrusive, Real-Time Detection of Acute Physiological Stress Levels during Training Exercises

Jeworutzki, André; Schwarzer, Jan; Luck, Kai von; Draheim, Susanne; Wang, Qi

doi:10.1145/3453892.3461833

Cited by 4 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A method for data collection and analysis has been developed and tested in initial trials based on a custom implementation of the ARC (see Figure 5). A preliminary set of statistical features from [16] was used to classify activities [23]. Since the data collected so far is small, new data will be collected in an upcoming study.…”

Section: Next Stepsmentioning

confidence: 99%

Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercises

Jeworutzki

2022

Designing Interactive Systems Conference

View full text Add to dashboard Cite

Stress is considered to have a bidirectional relationship with exercise injuries. This PhD project addresses the lack of research on methodological and measurement standards for determining acute physiological stress in the challenging context of repetitive training exercises such as squats, push-ups, and crunches. It explores how stress levels can be determined in real-time with both inertial and heart rate sensors, and how stress levels correlate with movement quality and exhaustion. To this end, a systematic method is elaborated to build a model capable of classifying stress levels with only wearable sensors in real-time. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Applied computing → Health informatics.

show abstract

Section: Next Stepsmentioning

confidence: 99%

Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercises

Jeworutzki

2022

Designing Interactive Systems Conference

View full text Add to dashboard Cite

show abstract

Lab Cultures – Hochschullabore im Kontext von Wissenstransfer und digitaler Innovation

Köhler¹,

Broscheit²,

Draheim³

et al. 2023

Transferinnovationen Und Innovationstransfer Zwischen Wissenschaft Und Wirtschaft

View full text Add to dashboard Cite

Trends in real-time artificial intelligence methods in sports: a systematic review

Vec,

Tomažič,

Kos

et al. 2024

J Big Data

View full text Add to dashboard Cite

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings.

show abstract

A Preliminary Experimental Outline to Train Machine Learning Models for the Unobtrusive, Real-Time Detection of Acute Physiological Stress Levels during Training Exercises

Abstract: A preliminary experimental outline to train machine learning models for the unobtrusive, real-time detection of acute physiological stress levels during training exercises. In PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference (pp. 575-584).

Cited by 4 publications

References 41 publications

Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercises

Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercises

Lab Cultures – Hochschullabore im Kontext von Wissenstransfer und digitaler Innovation

Trends in real-time artificial intelligence methods in sports: a systematic review

Contact Info

Product

Resources

About