A systematic approach with data mining for analyzing physical activity for an activity recognition system

Ghose, Supratip; Barua, Jagat Joyti

doi:10.1109/icaee.2013.6750374

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…A lot of research work has been done in this area, by employing machine learning algorithms on past user activity data [20], heart rate data [6], and accelerometer data [4,5] to identify the type of activity, and/or estimate caloric consumption. In [15], the authors use the phone accelerometer data and the WEKA tool to aggregate raw time series data and generate a predictive model for activity recognition. Contrary to prior work, this paper focuses on using a single device conveniently kept anywhere, rather than multiple devices distributed over the body for tracking purposes.…”

Section: Related Workmentioning

confidence: 99%

Social recommendations for personalized fitness assistance

Dharia

Eirinaki

Jain

et al. 2017

Pers Ubiquit Comput

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Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile, and keep track of their goals. However, due to the relatively high maintenance of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work we present the PROFit framework, a personalized fitness assistant aiming at engaging users in fitness activities, incorporating a social element. The PROFit architecture collects information from activity tracking devices and automatically classifies their activity type. Moreover, the framework incorporates a social recommender system. Using collaborative filtering on user profile and activity data PROFit generates personalized fitness schedules based on their availability and wellbeing goals. We also incorporate the social network community of the application's users and identify different tie strengths based on the user's connections and location. The output of the recommendation process is twofold, as both new activities, as well as fitness buddies, are being recommended to each user.

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Section: Related Workmentioning

confidence: 99%

Social recommendations for personalized fitness assistance

Dharia

Eirinaki

Jain

et al. 2017

Pers Ubiquit Comput

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“…Body model based AR [24] proposes on using body sensor to replace signal based ambient sensors to optimize recognition accuracy. The method also reduced the number of required wearable to achieve similar robustness, by using only accelerometer to estimate movement for data acquisition.…”

Section: Body Model Based Armentioning

confidence: 99%

Evaluating Activity Recognition Diversity as Readiness Measure Towards Mainstream Applicability

Aun¹,

Pamadass²,

Manickam³

2015

APAN Proceedings

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“…WEKA[12] -the library is used here as a reasoning mechanism. The user can implement selected methods and test their effectiveness.…”

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confidence: 99%

Extensible platform for studying the behavior of drivers in urban traffic

Błaszczyk

Turek

Cetnarowicz

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Research on traffic management and automation focuses on macro-scale problems involving large number of vehicles. It seems that the influence of individual decisions of particular drivers has not received sufficient attention over last decades. Understanding the characteristics of particular drivers is crucial for traffic optimization and for decisions making process performed by automated vehicles. In this paper we would like to present a prototype platform for studying characteristics of individual human drivers. The platform collects data during driving in urban environment and performs analysis and classification of observed behaviors. It is extensible in terms of measured parameters and classification methods. We also present results of first experiment using the created platform -classification of drivers during the left turn maneuver.

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A systematic approach with data mining for analyzing physical activity for an activity recognition system

Cited by 8 publications

References 13 publications

Social recommendations for personalized fitness assistance

Social recommendations for personalized fitness assistance

Evaluating Activity Recognition Diversity as Readiness Measure Towards Mainstream Applicability

Extensible platform for studying the behavior of drivers in urban traffic

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