Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

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Activity recognition using cell phone accelerometers

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

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

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A review of wearable sensors and systems with application in rehabilitation.

An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.

Wearable Sensors for Human Activity Monitoring: A Review

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems As a kind of user behavior, the transportation modes, such as walking, driving, etc, that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs The contribution of this work lies in the following two aspects On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement

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Understanding mobility based on GPS data

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network

Activity classification using realistic data from wearable sensors

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.

Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

We discuss health monitoring as a potential application field for wearable sensors. We present some usage models for health monitoring and discuss the technical requirements for the health-monitoring system based on wearable and ambient sensors, which measure health-related data in daily environments of the users or patients. The presentation is by no means complete, but it aims to give an idea of the system-level issues to be considered for real applications. The technology in this area is rapidly developing, and without doubt we will evidence emergence of these applications in the coming years in the market.

Health monitoring in the home of the future

The prevalence of lifestyle-related health problems is increasing rapidly. Many of the diseases and health risks could be prevented or alleviated by making changes toward healthier lifestyles. We have developed the Wellness Diary (WD), a concept for personal and mobile wellness management based on Cognitive-Behavioral Therapy (CBT). Two implementations of the concept were made for the Symbian Series 60 (S60) mobile phone platform, and their usability, usage, and acceptance were studied in two 3-month user studies. Study I was related to weight management and study II to general wellness management. In both the studies, the concept and its implementations were well accepted and considered as easy to use and useful in wellness management. The usage rate of the WD was high and sustained at a high level throughout the study. The average number of entries made per day was 5.32 (SD = 2.59, range = 0-14) in study I, and 5.48 (SD = 2.60, range = 0-17) in study II. The results indicate that the WD is well suited for supporting CBT-based wellness management.

Mobile Diary for Wellness Management—Results on Usage and Usability in Two User Studies

We study the possibility of applying an emerging RFID-based communication technology, NFC (Near Field Communication), to health monitoring. We suggest that NFC is, compared to other competing technologies, a high-potential technology for short-range connectivity between health monitoring devices and mobile terminals. We propose practices to apply NFC to some health monitoring applications and study the benefits that are attainable with NFC. We compare NFC to other short-range communication technologies such as Bluetooth and IrDA, and study the possibility of improving the usability of health monitoring devices with NFC. We also introduce a research platform for technical evaluation, applicability study and application demonstrations of NFC.

Juha Pärkkä

Papers

Activity classification using realistic data from wearable sensors

Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

Health monitoring in the home of the future

Mobile Diary for Wellness Management—Results on Usage and Usability in Two User Studies

Application of Near Field Communication for Health Monitoring in Daily Life