Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (author's final draft

Assistive technologies for the older people: Physical activity monitoring and fall detection

The advancements in information and communications technologies (ICT) and micro-nano manufacturing lead to innovative developments of smart sensors and intelligent devices as well as related assistive technologies which have been directly contributing to improving the life quality, from early detection of diseases to assisting daily living activities. Physical activity monitoring and fall detection are two specific examples where assistive technologies with the use of smart sensors and intelligent devices may play a key role in enhancing the life quality, especially improving the musculoskeletal health which is an essential aspect of health and wellbeing; and it is more important for the older people. This paper presents and dis-cusses about how sensors and wearable devices, such as accelerometers and mobile phones, may be employed to promote the musculoskeletal health. Assistive technologies and methods for physical activity monitoring and fall detection are discussed, with the focus on the fall detection using mobile phone technology, and assessments of the loading intensity of physical activity in a non-laboratory environment. The possible research directions, challenges and potential collaborations in the areas of assistive technologies and ICT solutions for the older populations are proposed and addressed

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Background: Tracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures. Objective: The objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern. Methods: Adults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1). Results: The mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812). Conclusions: The use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers

An Alert System for People Monitoring Based on Multi- Agents using Maps

This paper describes an alert system for people monitoring based on multi-agent using maps. This system monitors the users’ physical context using their mobile phone. The data acquisition is made using the available sensors on mobile phone. A set of agents on mobile phones are responsible for collecting, processing and sending data to the server. Another set of agents on server stores the data and checks the preconditions of the restrictions associated with the user, in order to trigger the appropriate alarms. These alarms are sent not only to the user that violates a restriction, but also to the one responsible for supervising the person monitored. The supervisor can control all the supervised people through a map interface with functionality such as sending a SMS or making a call directly from the map. The applicability of the system will be illustrated with an example for Alzheimer patient monitoring. These patients will carry on normal activity in the home environment or home for the elderly, monitored by their family or by nurses

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Motivational and Intervention Systems and Monitoring with mHealth Tools

Use of mobile and telecommunication technologies has become widespread in the last decade. With this development, use of mobile devices in healthcare (mHealth) is also increasing. Mobile phones, smartphones, and other mobile devices are affordable tools for different health-related services. In my research, with my research team, I have helped to develop several mHealth tools to address the quality of life of cancer survivors, cancer patients, and individuals at increased risk for cancer. Tobacco smoking is the major cause of several types of often-fatal cancers and cardio-respiratory diseases. Optimally, we hypothesize that the most effective mHealth tools should be customized and personalized. For smokers, the goal is to encourage cessation. For cancer survivors, one goal is to increase physical activity, which is associated with decreased rates of recurrent disease. In patients with incurable cancers, efficient and current monitoring of symptoms should contribute to better palliation. This dissertation explores multiple issues in use of mHealth tools with these medical populations. We discuss a general framework for collecting and managing healthcare data and mathematical models for data analysis. The specific contributions of this dissertation are: 1.) The design and development of a culturally tailored customized text messaging system for motivation and intervention; 2.) The design and development of a data collection system for an mHealth intervention, and; 3.) A model for monitoring pain levels using mobile devices