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93
Activity recognition using cell phone accelerometers
- Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data
, 2010
"... 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 (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, directio ..."
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Cited by 131 (8 self)
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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 (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., 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 10second 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 (e.g., 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.
A framework of energy efficient mobile sensing for automatic user state recognition
- IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS, AND SERVICES (MOBISYS
, 2009
"... Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices is difficult due to battery life li ..."
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Cited by 112 (7 self)
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Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices is difficult due to battery life limitations. In this paper, we present the framework design for an Energy Efficient Mobile Sensing System (EEMSS) that powers only necessary and energy efficient sensors and manages sensors hierarchically to recognize user state as well as detect state transitions. We also present the design, implementation, and evaluation of EEMSS that automatically recognizes user daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that it increases the smart phone’s battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.
Theory-driven design strategies for technologies that support behavior change in everyday life
- CHI '09: Proceedings of the 27th International Conference on Human Factors in Computing Systems
, 2009
"... In this paper, we propose design strategies for persuasive technologies that help people who want to change their everyday behaviors. Our strategies use theory and prior work to substantially extend a set of existing design goals. Our extensions specifically account for social characteristics and ot ..."
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Cited by 101 (3 self)
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In this paper, we propose design strategies for persuasive technologies that help people who want to change their everyday behaviors. Our strategies use theory and prior work to substantially extend a set of existing design goals. Our extensions specifically account for social characteristics and other tactics that should be supported by persuasive technologies that target long-term discretionary use throughout everyday life. We used these strategies to design and build a system that encourages people to lead a physically active lifestyle. Results from two field studies of the system—a three-week trial and a three-month experiment—have shown that the system was successful at helping people maintain a more physically active lifestyle and validate the usefulness of the strategies.
Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior
- IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B: CYBERNETICS
, 2009
"... We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational tim ..."
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Cited by 96 (24 self)
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We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees ’ self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = −0.55 (p <0.01), which has far-reaching implications for past and future research on social networks.
SociableSense: Exploring the Trade-offs of Adaptive Sampling and Computation Offloading for Social Sensing
- In Proc. of MobiCom’11. ACM
, 2011
"... The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data abo ..."
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Cited by 39 (6 self)
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The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
Activity and Gait Recognition with Time-Delay Embeddings
- in Proceedings of AAAI
, 2010
"... Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination of feature extraction using time-delay embedding and supervised learning. The computational requirements are considerably ..."
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Cited by 24 (1 self)
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Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination of feature extraction using time-delay embedding and supervised learning. The computational requirements are considerably lower than existing approaches, so the processing can be done in real time on a low-powered portable device such as a mobile phone. We evaluate the performance of our algorithm on a large, noisy data set comprising over 50 hours of data from six different subjects, including activities such as running and walking up or down stairs. We also demonstrate the ability of the system to accurately classify an individual from a set of 25 people, based only on the characteristics of their walking gait. The system requires very little parameter tuning, and can be trained with small amounts of data.
Distributed Recognition of Human Actions Using Wearable Motion Sensor Networks
, 2009
"... We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capabl ..."
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Cited by 23 (3 self)
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We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capable of rejecting outlying actions that are not in the training categories. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. We model the distribution of multiple action classes as a mixture subspace model, one subspace for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the sparse representation. Fast linear solvers are provided to compute such representation via ℓ 1-minimization. To validate the accuracy of the framework, a public wearable action recognition database is constructed, called wearable action recognition database (WARD). The database is comprised of 20 human subjects in 13 action categories. Using up to five motion sensors in the WARD database, DSC achieves state-of-the-art performance. We further show that the recognition precision only decreases gracefully using smaller subsets of active sensors. It validates the robustness of the distributed recognition framework on an unreliable wireless network. It also demonstrates the ability of DSC to conserve sensor energy for communication while preserve accurate global classification.
Ability-Based Design: Concept, Principles and Examples
- TACCESS
, 2011
"... Current approaches to accessible computing share a common goal of making technology accessible to users with disabilities. Perhaps because of this goal, they may also share a tendency to centralize disability rather than ability. We present a refinement to these approaches called ability-based desig ..."
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Cited by 18 (3 self)
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Current approaches to accessible computing share a common goal of making technology accessible to users with disabilities. Perhaps because of this goal, they may also share a tendency to centralize disability rather than ability. We present a refinement to these approaches called ability-based design that consists of focusing on ability throughout the design process in an effort to create systems that leverage the full range of human potential. Just as user-centered design shifted the focus of interactive system design from systems to users, ability-based design attempts to shift the focus of accessible design from disability to ability. Although prior approaches to accessible computing may consider users ’ abilities to some extent, ability-based design makes ability its central focus. We offer seven ability-based design principles and describe the projects that inspired their formulation. We also present a research agenda for ability-based design.
Musicalheart: A hearty way of listening to music,” in Sensys’ 12
- ACM
"... Abstract MusicalHeart is a bio-feedback based, context aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, EarBuddy, which consists of a pair of sensor equipped earphones that communicate to the smartphone via the audio jack. The EarBuddy plat ..."
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Cited by 14 (4 self)
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Abstract MusicalHeart is a bio-feedback based, context aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, EarBuddy, which consists of a pair of sensor equipped earphones that communicate to the smartphone via the audio jack. The EarBuddy platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user, while the user is listening to music. The physiological information along with contextual information are then sent to a remote server, which provides dynamic music suggestions to assist the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% − 85% correlated to the ground truth with an average error of 7.5 BPM, and the accuracy of activity level and context inference are on average 96.8% and 84.1%, respectively. We demonstrate the practicality of MusicalHeart by deploying it to two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and it's quality of recommendation improves over time.
Jog Falls: A Pervasive Healthcare Platform for Diabetes Management
"... Abstract. This paper presents Jog Falls, an end to end system to manage diabetes that blends activity and energy expenditure monitoring, diet-logging, and analysis of health data for patients and physicians. It describes the architectural details, sensing modalities, user interface and the physician ..."
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Cited by 14 (0 self)
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Abstract. This paper presents Jog Falls, an end to end system to manage diabetes that blends activity and energy expenditure monitoring, diet-logging, and analysis of health data for patients and physicians. It describes the architectural details, sensing modalities, user interface and the physician’s backend portal. We show that the body wearable sensors accurately estimate the energy expenditure across a varied set of active and sedentary states through the fusion of heart rate and accelerometer data. The GUI ensures continuous engagement with the patient by showing the activity goals, current and past activity states and dietary records along with its nutritional values. The system also provides a comprehensive and unbiased view of the patient’s activity and food intake trends to the physician, hence increasing his/her effectiveness in coaching the patient. We conducted a user study using Jog Falls at Manipal University, a leading medical school in India. The study involved 15 participants, who used the system for 63 days. The results indicate a strong positive correlation between weight reduction and hours of use of the system.
