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The number of home office workers sitting for many hours is increasing. The sensor chair is tracking users’ sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. The system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. Data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. The sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. The classifier achieves an accuracy of 97.4% on our test set and is comparable to the performance of the random forest with 98.9%.
A significant proportion of road traffic accidents are due to inattentiveness or fatigue at the wheel. Approaches to monitoring the driver's condition range from eye tracking and driving behavior analysis to yawn and blink detection and ECG measurement. This work describes the development of a mobile system for the measurement and processing of ECG data. The aim of the signal processing is to quantify the driver’s fatigue with the heartrate variability (HRV). The work includes the hardware and software design of the sensor. First, the development of low-noise electronics including AD conversion is described. Then the software signal processing with QRS complex detection and plotting front end is explained. The resulting sensor is compact, low-cost and provides a good signal for HRV extraction.
Sleep is an important part of our life that significantly influences our health and well-being. The monitoring of sleep can provide data based on which sleep quality could be improved. This paper presents a system for heart rate detection during sleep. The data is collected from sensors underneath the test subjects. Though the data contains noise, it needs to be filtered to remove it. Due to the low strength of the signals, they need to be amplified after filtering. At some points of the signal, particular heartbeats may not be tracked by sensors due to the failure of a sensor or other reasons, which should be considered. The heart rate is detected in intervals of 15 s. A tool is implemented that detects the heart rate and visualizes it. The preprocessing of the data is performed with several filters: a highpass filter, a band-reject filter, a lowpass filter, and a motion detector. After the preprocessing of the data, the quality of the signal is significantly increased, and detection is possible.
Deep Learning-based EEG Detection of Mental Alertness States from Drivers under Ethical Aspects
(2022)
One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. The path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. The key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). The gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. This research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). The classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. The method is linked to the simulated driving scenario for which it was developed. This leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.
The goal of this paper pretends to show how a bed system with an embedded system with sensor is able to analyze a person’s movement, breathing and recognizing the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors.
The overall goal of this work is to detect and analyze a person's movement, breathing and heart rate during sleep in a common bed overnight without any additional physical contact. The measurement is performed with the help of
sensors placed between the mattress and the frame. A two-stage pattern classification algorithm based has been implemented that applies statistics analysis to recognize the position of patients. The system is implemented in a sensors-network, hosting several nodes and communication end-points to support quick and efficient classification. The overall tests show convincing results for the position recognition and a reasonable overlap in matching.
These days computer analysis of ECG (Electrocardiograms) signals is common. There are many real-time QRS recognition algorithms; one of these algorithms is Pan-Tompkins Algorithm. Which the Pan-Tompkins Algorithm can detect QRS complexes of ECG signals. The proposed algorithm is analysed the data stream of the heartbeat based on the digital analysis of the amplitude, the bandwidth, and the slope. In addition to that, the stress algorithm compares whether the current heartbeat is similar or different to the last heartbeat after detecting the ECG signals. This algorithm determines the stress detection for the patient on the real-time. In order to implement the new algorithm with higher performance, the parallel programming language CUDA is used. The algorithm determines stress at the same time by determining the RR interval. The algorithm uses a different function as beat detector and a beat classifier of stress.