Smart home resident identification based on behavioral patterns using ambient sensors

Lesani, Fatemeh Sadat; Ghazvini, Faranak Fotouhi; Amirkhani, Hossein

doi:10.1007/s00779-019-01288-z

Cited by 7 publications

(3 citation statements)

References 56 publications

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“…Differently from the datasets mentioned in Table 3 , these datasets usually result from long-term data collections. As detailed by Lesani et al [ 59 ], the Twor2009, Tulum2009 and Tulum2010 datasets, from the CASAS project, were collected in periods ranging from 3 to 6 months, in which information from motion, binary, door and item sensors were recorded.…”

Section: Datasets Of Human Activitiesmentioning

confidence: 99%

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Ranieri

MacLeod

Dragone

et al. 2021

Sensors

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Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

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Section: Datasets Of Human Activitiesmentioning

confidence: 99%

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Ranieri

MacLeod

Dragone

et al. 2021

Sensors

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“…By discovering the common activity patterns for everyone instead of using pre-selected activities, DVSM could also address the issue of inter-subject variability such as different eating styles in individuals. The “Bag of Sensor Events” strategy is another novel feature engineering approach which is introduced in [ 38 ] to address the challenge of performing different actions in different order. The uncertainty in doing the activities could be decreased by taking the advantages of the combination of captured data via different sensors located in different places.…”

Section: Related Workmentioning

confidence: 99%

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

Shirali

Bayo-Monton

Fernández-Llatas

et al. 2020

Sensors

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Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents’ health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident’s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.

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“…Another paper is by Lesani et al [12] entitled "Smart home resident identification based on behavioral patterns using ambient sensors". In this paper, the authors present a novel approach to identify the smart home residents.…”

mentioning

confidence: 99%

Special issue on trends & advances to mine intelligence from ambient data

Yasar

Malik²,

Shakshuki

et al. 2021

Pers Ubiquit Comput

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Smart home resident identification based on behavioral patterns using ambient sensors

Cited by 7 publications

References 56 publications

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

Special issue on trends & advances to mine intelligence from ambient data

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