The Benefits of Personalized Smartphone-Based Activity Recognition Models

Lockhart, Jeffrey W.; Weiss, Gary M.

doi:10.1137/1.9781611973440.71

Cited by 55 publications

(32 citation statements)

References 16 publications

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“…In order to recognize personal, unseen activities, some incremental methods [22], [1] are proposed. However, their results are similar to those of non-personalized models [12], indicating that the feature selection is crucial for activity recognition [12]. The semantic attribute sequence based models are also used for recognizing unseen new activities [5], [4], but still fail to consider the influence of different features.…”

Section: Introductionmentioning

confidence: 89%

Adaptive Activity Recognition with Dynamic Heterogeneous Sensor Fusion

Zhang¹,

Wang²,

Nguyen³

et al. 2014

Proceedings of the 6th International Conference on Mobile Computing, Applications and Services

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In spite of extensive research in the last decade, activity recognition still faces many challenges for real-world applications. On one hand, when attempting to recognize various activities, different sensors play different on different activity classes. This heterogeneity raises the necessity of learning the optimal combination of sensor modalities for each activity. On the other hand, users may consistently or occasionally annotate activities. To boost recognition accuracy, we need to incorporate the user input and incrementally adjust the model. To tackle these challenges, we propose an adaptive activity recognition with dynamic heterogeneous sensor fusion framework. We dynamically fuse various modalities to characterize different activities. The model is consistently updated upon arrival of newly labeled data. To evaluate the effectiveness of the proposed framework, we incorporate it into popular feature transformation algorithms, e.g., Linear Discriminant Analysis, Marginal Fisher's Analysis, and Maximum Mutual Information in the proposed framework. Finally, we carry out experiments on a real-world dataset collected over two weeks. The result demonstrates the practical implication of our framework and its advantage over existing approaches.

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Section: Introductionmentioning

confidence: 89%

Adaptive Activity Recognition with Dynamic Heterogeneous Sensor Fusion

Zhang¹,

Wang²,

Nguyen³

et al. 2014

Proceedings of the 6th International Conference on Mobile Computing, Applications and Services

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show abstract

“…On the other hand, Random Forest is relevantly a new classification algorithm. As mentioned, it basically uses multiple decision trees for classification by using the method of "bagging" and it has been successfully applied to mobile activity recognition [15], [16].…”

Section: B Activity Recognitionmentioning

confidence: 99%

Phone position/placement detection using accelerometer: Impact on activity recognition

Coskun

İncel

Özgövde

2015

2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Smart phone platforms, equipped with a rich set of sensors enable mobile sensing applications that support users for both personal sensing and large-scale community sensing. In such mobile sensing applications, the position/placement of the phone relative to the user body provides valuable context information. For example, in physical activity recognition using motion sensors, the position of the phone provides important information, since the sensors generate different signals when the phone is carried in different positions and this makes it difficult to successfully identify the activities with sensor data coming from different positions. In this paper, we investigate whether it is possible to successfully identify phone positions using only accelerometer data which is the most commonly used sensor on physical activity recognition studies, rather than using additional sensors. Additionally, we explore how much this position information increases the activity recognition accuracy compared with position independent activity recognition. For this purpose, we collected activity data from 15 participants carrying three phones in different positions, performing activities of walking, running, sitting, standing, climbing up/down stairs, transportation with a bus, making a phone call, interacting with an application on the smart phone, sending an SMS. The collected data is processed with the Random Forest classifier. According to the results of position recognition, using basic accelerometer features which are also used in the activity recognition, can achieve an accuracy of 77.34%, however, this ratio increases to 85% when basic features are combined with angular features calculated from the orientation of the phone. According to the results of the activity recognition experiments, on average the results are similar for position specific and position independent recognition. Only for the pocket case, 2% increase was observed.

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“…For instance, [13] pre-processed phone activities of one million users to obtain information about their approximative temporal location, then mined daily motifs from the spatio-temporal data to infer human activities. Finally, smart phones are or will be equipped with accelerometers and/or gyroscopes providing data about physical activities of users: [15] suggest a complete system of activity recognition based on smartphone accelerometers with potential application to health monitoring.…”

Section: Related Workmentioning

confidence: 99%

Country-Scale Exploratory Analysis of Call Detail Records Through the Lens of Data Grid Models

Guigourès¹,

Gay

Boullé

et al. 2015

Machine Learning and Knowledge Discovery in Databases

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International audienceCall Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination , date and time of calls. CDRs data analysis has received much attention in the recent years since it might reveal valuable information about human behavior. It has shown high added value in many application domains like e.g., communities analysis or network planning. In this paper, we suggest a generic methodology based on data grid models for summarizing information contained in CDRs data. The method is based on a parameter-free estimation of the joint distribution of the variables that describe the calls. We also suggest several well-founded criteria that allows one to browse the summary at various granularities and to explore the summary by means of insightful visualizations. The method handles network graph data, temporal sequence data as well as user mobility data stemming from original CDRs data. We show the relevance of our methodology on real-world CDRs data from Ivory Coast for various case studies, like network planning strategy and yield management pricing strategy

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The Benefits of Personalized Smartphone-Based Activity Recognition Models

Cited by 55 publications

References 16 publications

Adaptive Activity Recognition with Dynamic Heterogeneous Sensor Fusion

Adaptive Activity Recognition with Dynamic Heterogeneous Sensor Fusion

Phone position/placement detection using accelerometer: Impact on activity recognition

Country-Scale Exploratory Analysis of Call Detail Records Through the Lens of Data Grid Models

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