Abstract
Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.
Highlights
Introduction and MotivationThere is growing an amount of accessories and safety equipment designated to improve environmental conditions for workers in different sectors
Regarding Machine Learning models, our objective was to present a combination of models on the basis of supervised and unsupervised learning in order to decrease the classification noise, since we trained a model of time series with all the data from the wearable device; due to the nature of the information, our performance in evaluation was little more than 60%, which is why we decided to split the present work
Numerous works exploring the use of techniques that focus on Naive Bayes (NB) [58], Decision Trees [59], Hidden Markov Modeling (HMM) [60], Neural Networks (NN) [61], Support Vector Machines (SVM) [62], and Deep Belief Networks (DBN) [63] are some of the most relevant implementations
Summary
Platforms implementing predictive maintenance strategies have been introduced on the market [20,21,22,23,24]. Numerous works exploring the use of techniques that focus on Naive Bayes (NB) [58], Decision Trees [59], Hidden Markov Modeling (HMM) [60], Neural Networks (NN) [61], Support Vector Machines (SVM) [62], and Deep Belief Networks (DBN) [63] are some of the most relevant implementations Another noteworthy application is the use of a three-axis accelerometer, which, helped by an algorithm, can detect the number of steps taken and monitor the user’s physical activity [45,46,47,49] and even detect falls [53], epileptic seizures [43], and degrees of shaking that happen to people with Parkinson’s disease [64].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
