An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Objective measures, sensors and computational techniques for stress recognition and classification

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

/pdf/data-mining-for-wearable-sensors-in-health-monitoring-13a1q3cm4g.pdf

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Activity detection and classification are very important for autonomous monitoring of humans for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors have found wide-spread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, such as accelerometer, gyroscope, and camera, it has become more feasible to develop activity monitoring algorithms employing one or more of these sensors with increased accessibility. We provide a complete and comprehensive survey on activity classification with wearable sensors, covering a variety of sensing modalities, including accelerometer, gyroscope, pressure sensors, and camera- and depth-based systems. We discuss differences in activity types tackled by this breadth of sensing modalities. For example, accelerometer, gyroscope, and magnetometer systems have a history of addressing whole body motion or global type activities, whereas camera systems provide the context necessary to classify local interactions, or interactions of individuals with objects. We also found that these single sensing modalities laid the foundation for hybrid works that tackle a mix of global and local interaction-type activities. In addition to the type of sensors and type of activities classified, we provide details on each wearable system that include on-body sensor location, employed learning approach, and extent of experimental setup. We further discuss where the processing is performed, i.e., local versus remote processing, for different systems. This is one of the first surveys to provide such breadth of coverage across different wearable sensor systems for activity classification.

A Survey on Activity Detection and Classification Using Wearable Sensors

Acute mental stress assessment via short term HRV analysis in healthy adults : a systematic review with meta-analysis

A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals

Vehicular accidents are increasingly contributing towards loss of lives across the world. Timely detection of physiological and psychological parameters of the vehicular driver, which could cause various levels of physical and mental fatigue that lead to slower reflexes is therefore extremely important. As part of an ambitious research initiative, India is developing a pervasive computing solution for eliminating / reducing such accidents. As one of the component of such solution, a wearable computing system has been envisioned to be worn by the driver. A complex set of noninvasive and nonintrusive sensor-compute element integrated with appropriate e-textile would form the primary part of this wearable computer.Out of the initial set of physiological parameters such as Skin Conductance, Oximetry Pulse, Respiration, SPO2, the current work focuses on the first two parameters to detect and monitor the mental fatigue / drowsiness of a driver. Using Neural Network approach, Multilayer Perceptron Neural Networks (MLP NN) have been designed to classify Pre and Posting driving fatigue levels. The performance of single hidden layer and two hidden layers based MLP NN have been discussed using the performance measures such as, Percentage Classification Accuracy (PCLA), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Area under Receiver Operating Characteristic Curve (AROC), Area under Convex Hull of ROC (AHROC). It was discovered that the performance of one hidden layer based MLP NN is comparable to the two hidden layers based MLP NN and there is slight rise in PCLA from One hidden layer to two hidden layer.

Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach

Monitoring driver fatigue, inattention, drowsiness and alertness is very important in order to prevent vehicular accidents. The system detecting and monitoring should be noninvasive type and non-distracting to the driver. The physiological parameters such as skin conductance, oximetry pulse, respiration, SPO2 and BVP can lead to the acceptable solution to the problem. The author is working on the subset of the project 'BITS Life Guard system' and trying to correlate the fatigue of a driver with the set of physiological parameters so as to fulfill the requirements. This paper is an attempt towards finding the correlation of skin conductance with the fatigue of a driver. Artificial Neural Network approach is used to design the system by taking actual body parameters of the drivers under different state of work & environment. Multilayer Perceptron (MLP) Neural Network (NN) and the Support Vector Machine (SVM) are used to correlate the driver's fatigue level with skin conductance. Two state classifiers were designed and tested with 18 input features for 2392 total data rows and found that SVM gives a better Classification Accuracy. The performance measures used for designing are Percentage Classification Accuracy (PCLA), Mean Square Error (MSE) and Receiver Operating Characteristics (ROC).

An SVM Classifier for Fatigue-Detection Using Skin Conductance for Use in the BITS-Lifeguard Wearable Computing System

Fast and credible identification and estimation of driver's stress-level and stress-type from sensed physiological signals has been one of the critical research areas in the recent past. Several good metrics and mechanisms involving bioelectric signals like the Galvanic Skin Response (GSR), Electrocardiogram (ECG) and the Photoplethysmography (PPG) have been identified by the scholars over the years. This paper discusses the features extracted from physiological data collected in five different scenarios and their usefulness with the help of statistical trend analysis methods. The algorithm developed comprises of a novel shape-based feature weight allocation approach and a technique for credible online realtime stress-trend detection. Such a stress-trend detection by the mesh of embedded sensory elements residing in the e-fabric of a wearable computing system will help in reducing chances of fatal driving errors by the way of in-time activation of alerts and actuation of corresponding safety / recovery procedures.

An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers

Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four stress-classes were identified using cascade forward neural network (CASFNN) which performed consistently with minimal intra- and inter-subject variability. Task-induced stress-trends were tracked using Triggs’ Tracking Variable-based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.

Rahul Banerjee

Papers

A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals

Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach

An SVM Classifier for Fatigue-Detection Using Skin Conductance for Use in the BITS-Lifeguard Wearable Computing System

An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios