Eliasz Kantoch

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.

Monitoring activities of daily living based on wearable wireless body sensor network.

With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services.

Wearable Patient Home Monitoring Based on ECG and ACC Sensors

Advanced developments in existing information technologies are extending the range of treatment and health services available at patients home. This paper proposes a mobile monitoring system that integrates wearable ECG and ACC mobile sensors. We present an algorithm determining the correlation between the heart rate and movements based on automatic analysis of ACC and ECG signals. The system was tested on seven healthy adults who were asked to perform normal daily activity. As a result, we examined general physi- cal state during normal daily activities of the subject, based on the analysis of sensor signals.

Wearable mobile network as an integrated part of assisted living technologies

Advanced developments in existing information technologies are extending the range of treatment and health services available at patients home. Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into clothing what opens a wide range of possibilities for health monitoring. This paper discusses existing solution in field of intelligent clothing and examines technical possibilities of integrating physiological sensors into clothing. It describes developments and trends all over the world in the areas of mobile sensors, telemedicine and smart fabrics. Finally, this paper presents the design of a health monitoring system based on sensors included in clothes.

Eyetracking-based assessment of affect-related decay of human performance in visual tasks

Abstract Computerized recognition of human emotions enriches the man–machine interaction with a new personal and behavioral aspect. Firstly, because the influence of emotions on the human performance can be objectively assessed and predicted using standardized stimuli, secondly, because an emotion-sensitive computer system can be programmed to react adequately. This paper presents an investigation of emotional influence on the human scanpath. The proposed method assumes that the emotional state of the observer can distract his or her visual attention and can be reliably expressed in parameters of eye movements. We performed visual task experiments in order to record scanpaths from volunteers under auditory stress of controlled intensity. We used a calibrated set of stimuli and controlled the response of volunteers’ central nervous system expressed by the heart rate. For each efficiently stimulated participant, we related the scanpath parameters to the accuracy of solving a given task and to participant’s comment. As a principal result, we obtained a significant change of (1) saccade frequency in 90% and (2) maximum velocity in fixation phase in 80% of participants under stress. These results prove the influence of emotions on visual acuity, feasibility of eyetracking-based assessment of emotional state and motivate further investigations.

BAN-based health telemonitoring system for in-home care

The aim of the research is to build a prototype of the BAN-based monitoring system for in-home care. The system consists of body central unit with Bluetooth module and selected both wearable and stationary sensors that monitor human physiological and movement parameters. Body central unit acquires data from wearable sensors and transmit it wirelessly to the home base station for analysis and storage. The home base station sends data to the remote medical server where they are available via web based graphical interface. The system was tested by six volunteers in home environment. The maximum measured packet loss rate was 2%. Experimental results showed that proposed system was feasible and can provide with information about selected health parameters to physicians or family what can improve the patient quality of life.

Technical Verification of Integrating Wearable Sensors into BSN-Based Telemedical Monitoring System

With recent advances in electronics, wireless communication and computer science it is possible to develop miniaturized pervasive health monitoring devices that are capable of monitoring physiological signals during daily life activities. The aim of the research is to implement and test health monitoring system to monitor and analyze humans physiological signals. System consists of body central unit with Blue tooth module and wearable sensors: ECG sensor, temperature sensor, skin humidity sensor and accelerometers placed on the human body or integrated with clothes and network gateway to forward data to remote medical server. The system includes specially designed transmission protocol and remote web-based graphical user interface for real time data analysis. Experimental results for a group of human who performed various activities (ex. working, running etc.) are compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and open new opportunities in developing novel healthcare services.

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.

A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal

The analysis of phonocardiogram (PCG), although considered as well established in a clinical application, still constitutes the valuable source of diagnostic data. Currently, electronic auscultation provides digital signals which can be processed in order to automatically evaluate the condition of heart or lungs. In this paper, we propose a novel approach for the classification of phonocardiographic signals. We extracted a set of time-frequency parameters which enable to effectively differentiate between normal and abnormal heart beats (with valve defects). These features have constituted an input of the convolutional neural network, which we used for classification of pathological signals. The Aalborg University heart sounds database from PhysioNet/Computing in Cardiology Challenge 2016 was used for verification of developed algorithms. We obtained 99.1% sensitivity and 91.6% specificity on the test data, which is motivational for further research.

Bio-inspired Topology of Wearable Sensor Fusion for Telemedical Application

Application of wearable sensors is a promising approach in building novel telemedical services. In this paper, we propose the biologically inspired method for monitoring human activity in living conditions. The solution is based on the set of sensors integrated in the single wearable device and imitates the natural arrangement of human perception system. The designed wearable device enables to acquire physiological and environmental parameters. With the use of proposed appliance it is possible to collect body and ambient temperature, barometric pressure, light intensity and acceleration. In the experimental part, the signals were recorded during selected activities of daily living (ADL). The sitting activity classification was implemented using perceptron.