Activity Recognition in the Home Using Simple and Ubiquitous Sensors

Tapia, Emmanuel Munguia; Intille, Stephen; Larson, Kent

doi:10.1007/978-3-540-24646-6_10

Cited by 1,021 publications

(711 citation statements)

References 11 publications

(8 reference statements)

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“…A dense sensing platform is used, such as contact switch sensors to monitor the opening and closing of doors, motion sensors to detect the user presence at a particular location, or pressure sensors to indicate the usage of objects, bed or sofa [12,31,32]. Switch sensors deployed in multiple objects in a home such as doors, windows, cupboards and refrigerator, can be used in the NB classifier based recognition approaches [12,30], where NB identifies the activity corresponding to the sensor values with the highest probability. PNN classifier [15,33] derived from Bayesian and Fisher discriminant analysis (FDA) can be applied to estimate the likelihood of a sample being part of a learned activity class.…”

Section: Related Workmentioning

confidence: 99%

“…Activity segmentation is performed using different techniques, sliding windows [7], relative weighting of objects in adjacent activities [8] or pattern mining [9], just to name a few. Segmented activity instances are classified in activity classes using different learning models such as Hidden Markov Model (HMM) [10], Conditional Random Fields (CRF) [11], Naive Bayes (NB) [12], Support Vector Machine (SVM) [13], Artificial Neural Network (ANN) [14,15], and Decision Tree (DT) [16]. In activity classification, a false assignment could occur due to the unreliable nature of sensor data [17], incorrect execution of an activity [18], similar activities due to overlapping in features [19] or inability of a learning algorithm to assign the correct label [20].…”

Section: Introductionmentioning

confidence: 99%

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Activity recognition in smart homes with self verification of assignments

2015

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractActivity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Activity recognition in smart homes with self verification of assignments

2015

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“…For example, the sociometer [Choudhury and Pentland 2003] is a wearable sensor package, which is used to monitor face-to-face interactions and social dynamics. PlaceLab [Larson and Intille ] is an example of a "living lab", where hundreds of sensors are built into objects and the home environment (as opposed to wearable sensors) for various research purposes including activity recognition [Intille et al 2006,Logan et al 2007,Tapia et al 2004. Similar work has been done in an office space in which hundreds of motion sensors were used to study the social interactions and behaviors of approximately 100 subjects [Wren et al 2007].…”

Section: Related Workmentioning

confidence: 99%

Discovering routines from large-scale human locations using probabilistic topic models

Farrahi

Gática-Pérez

2011

ACM Trans. Intell. Syst. Technol.

193

109

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In this work we discover the daily location-driven routines which are contained in a massive reallife human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16 month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include "going to work late", "going home early", "working non-stop" and "having no reception (phone off)" at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as "going to work early" or "turning off the mobile (or having no reception) in the evenings". We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.

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“…Mckeever et al [8] also used the Van Kasteren data set [13]. Tapia et al [12] used environmental change sensors that had been installed on doors, windows, cabinets, drawers etc. Lepri et al [5] recognized the ongoing activities by using the visual sensors.…”

Section: Related Workmentioning

confidence: 99%

Identifying Important Action Primitives for High Level Activity Recognition

Manzoor

Villalonga

Calatroni

et al. 2010

Lecture Notes in Computer Science

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Abstract. Smart homes have a user centered design that makes human activity as the most important type of context to adapt the environment according to people's needs. Sensor systems that include a variety of ambient, vision based, and wearable sensors are used to collect and transmit data to reasoning algorithms to recognize human activities at different levels of abstraction. Despite various types of action primitives are extracted from sensor data and used with state of the art classification algorithms there is little understanding of how these action primitives affect high level activity recognition. In this paper we utilize action primitives that can be extracted from data collected by sensors worn on human body and embedded in different objects and environments to identify how various types of action primitives influence the performance of high level activity recognition systems. Our experiments showed that wearable sensors in combination with object sensors clearly play a crucial role in recognizing high level activities and it is indispensable to use wearable sensors in smart homes to improve the performance of activity recognition systems.

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Activity Recognition in the Home Using Simple and Ubiquitous Sensors

Cited by 1,021 publications

References 11 publications

Activity recognition in smart homes with self verification of assignments

Activity recognition in smart homes with self verification of assignments

Discovering routines from large-scale human locations using probabilistic topic models

Identifying Important Action Primitives for High Level Activity Recognition

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