Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector
Machine (SVM), are also acknowledged.

A public domain dataset for human activity recognition using smartphones

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http://ieeexplore.ieee.org/iel6/29/26151/01163502.pdf

Digital control of dynamic systems

Direct measurement of human movement by accelerometry

Despite much progress with physical activity assessment, the limitations concerning the accurate measurement of physical activity are often amplified in young people due to the cognitive, physiological, and biomechanical changes that occur during natural growth as well as a more intermittent pattern of habitual physical activity in youth compared with adults. This mini-review describes and compares methods to assess habitual physical activity in youth and discusses main issues regarding the use and interpretation of data collected with these techniques. Self-report instruments and movement sensing are currently the most frequently used methods for the assessment of physical activity in epidemiological research; others include heart rate monitoring and multisensor systems. Habitual energy expenditure can be estimated from these input measures with varying degree of uncertainty. Nonlinear modeling techniques, using accelerometry perhaps in combination with physiological parameters like heart rate or temperature, have the greatest potential for increasing the prediction accuracy of habitual physical activity energy expenditure. Although multisensor systems may be more accurate, this must be balanced against feasibility, a balance that shifts with technological and scientific advances and should be considered at the beginning of every new study.

Assessment of physical activity in youth

http://sclab.yonsei.ac.kr/courses/10TPR/10TPR.files/Using%20acceleration%20measurements%20for%20activity%20recognition=%20An%20effective%20learning%20algorithm%20for%20constructing%20neural%20classifiers.pdf

Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

This paper presents a fully automated recurrent neural network (FARNN) that is capable of self-structuring its network in a minimal representation with satisfactory performance for unknown dynamic system identification and control. A novel recurrent network, consisting of a fully-connected single-layer neural network and a feedback interconnected dynamic network, was developed to describe an unknown dynamic system as a state-space representation. Next, a fully automated construction algorithm was devised to construct a minimal state-space representation with the essential dynamics captured from the input-output measurements of the unknown system. The construction algorithm integrates the methods of minimal model determination, parameter initialization and performance optimization into a systematic framework that totally exempt trial-and-error processes on the selections of network sizes and parameters. Computer simulations on benchmark examples of unknown nonlinear dynamic system identification and control have successfully validated the effectiveness of the proposed FARNN in constructing a parsimonious network with superior performance

A fully automated recurrent neural network for unknown dynamic system identification and control

Online classifier construction algorithm for human activity detection using a tri-axial accelerometer

This paper presents a neuro-fuzzy classifer for activity recognition using one triaxial accelerometer and feature reduction approaches. We use a triaxial accelerometer to acquire subjects' acceleration data and train the neurofuzzy classifier to distinguish different activities/movements. To construct the neuro-fuzzy classifier, a modified mapping-constrained agglomerative clustering algorithm is devised to reveal a compact data configuration from the acceleration data. In addition, we investigate two different feature reduction methods, a feature subset selection and linear discriminate analysis. These two methods are used to determine the significant feature subsets and retain the characteristics of the data distribution in the feature space for training the neuro-fuzzy classifier. Experimental results have successfully validated the effectiveness of the proposed classifier.

/pdf/activity-recognition-using-one-triaxial-accelerometer-a-175n7yz8dr.pdf

Activity recognition using one triaxial accelerometer: a neuro-fuzzy classifier with feature reduction

This paper presents a Hammerstein recurrent neurofuzzy network associated with an online minimal realization learning algorithm for dealing with nonlinear dynamic applications. We fuse the concept of states in linear systems into a neurofuzzy framework so that the whole structure can be expressed by a state-space representation. An online minimal realization learning algorithm has been developed to find a controllable and observable state-space model of minimal size from the input-output measurements of a given system. Such an idea can simultaneously resolve the problem of the determination of a minimal structure and the difficulty of network stability analysis. The advantages of our approach include: 1) our recurrent network is capable of translating the complicated dynamic behavior of a nonlinear system into a minimal set of linguistic fuzzy dynamical rules and into state-space representation as well and 2) an online minimal realization learning algorithm unifies an order determination algorithm, a hybrid parameter initialization method, and a recursive recurrent learning algorithm into a systematic procedure to identify a minimal structure with satisfactory performance. Performance evaluations on benchmark examples as well as real-world applications have successfully validated the effectiveness of our approach.

Yen-Ping Chen

Papers

Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

A fully automated recurrent neural network for unknown dynamic system identification and control

Online classifier construction algorithm for human activity detection using a tri-axial accelerometer

Activity recognition using one triaxial accelerometer: a neuro-fuzzy classifier with feature reduction

A Hammerstein Recurrent Neurofuzzy Network With an Online Minimal Realization Learning Algorithm