Towards the Combination of Statistical and Symbolic Techniques for Activity Recognition Extended Abstract

Techniques for activity recognition are fundamental components of any context-aware system. Indeed, precise knowledge of the user’s current activity is necessary in order to thoroughly tailor services to the user’s context

Stair Gait Classification from Kinematic Sensors

Gait measurement is of interest for both orthopedists and biomechanical engineers. It is useful for analysis of gait disorders and in design of orthotic and prosthetic devices. In this chapter an algorithm is presented to suit estimation of one foot angle in the sagital plane, independent on gait conditions. Only one gyro is used during swing and two accelerometers are needed for calibration during stance. Also, the sensor placement at the front foot avoids the need for heel strike for stance transition. Stair walking can therefore be studied. From the estimated swing trajectory three different gait conditions: up stair, horizontal and down stair are classified.Abstracting and non-profit use of the material is permitted with the credit to the source. After this work has been published by the I-Tech Education and Publishing, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. (www.ars-journal.com)</p

A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

BACKGROUND: 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 a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. METHODS: Using off-the-shelf wireless sensors we designed a prototype WBAN which features a standard ZigBee compliant radio and a common set of physiological, kinetic, and environmental sensors. RESULTS: We introduce a multi-tier telemedicine system and describe how we optimized our prototype WBAN implementation for computer-assisted physical rehabilitation applications and ambulatory monitoring. The system performs real-time analysis of sensors' data, provides guidance and feedback to the user, and can generate warnings based on the user's state, level of activity, and environmental conditions. In addition, all recorded information can be transferred to medical servers via the Internet and seamlessly integrated into the user's electronic medical record and research databases. CONCLUSION: WBANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for prolonged periods of time. To make this technology ubiquitous and affordable, a number of challenging issues should be resolved, such as system design, configuration and customization, seamless integration, standardization, further utilization of common off-the-shelf components, security and privacy, and social issues

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352

A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data

The profusion of wireless enabled mobile devices in daily life routine and advancement in pervasive computing has opened new horizons to analyse and model the contextual information. This contextual information (for example, proximity data and location information) can be very helpful in analysing the human behaviours. Wireless proximity data can provide important information about the behaviour and daily life routines of an individual. In this paper, we used Bluetooth proximity data to validate this concept by detecting repeated activity patterns and behaviour of low entropy mobile people by using n-gram and correlative matrix techniques. Primary purpose is to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Repeated patterns found in Bluetooth proximity data can also show the long term routines such as, monthly or yearly patterns in an individual's daily life that can further help to analyse more complex and abnormal routines of human behaviour

Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities within the Home

One of the key objectives of an ambient assisted living environment is to enable elderly people to lead a healthy and independent life. These assisted environments have the capability to capture and infer activities performed by individuals, which can be useful for providing assistance and tracking functional decline among the elderly community. This paper presents an activity recognition engine based on a hierarchal structure, which allows modelling, representation and recognition of ADLs, their associated tasks, objects, relationships and dependencies. The structure of this contextual information plays a vital role in conducting accurate ADL recognition. The recognition performance of the inference engine has been validated with a series of experiments based on object usage data collected within the home environment

Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost