Daily living activity recognition with ECHONET Lite appliances and motion sensors

Moriya, Kazuki; Nakagawa, Eri; Fujimoto, Manato; Suwa, Hirohiko; Arakawa, Yutaka; Kimura, Aki; Miki, Satoko; Yasumoto, Keiichi

doi:10.1109/percomw.2017.7917603

Cited by 23 publications

(13 citation statements)

References 12 publications

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“…We consider that the discomfort to the user may be reduced by performing the active control of lighting devices while avoiding the specific activity recognized by certain methods. (8,9)…”

Section: Limitationmentioning

confidence: 99%

“…Therefore, studies on activity recognition utilizing indoor location information have been conducted actively in recent years. (8,9) As a representative example of an activity recognition study, Ueda et al (10) have proposed an effective method for accurately recognizing 10 types of daily living activity, such as cooking, watching TV, and taking a bath, by utilizing information of indoor locations and the power consumption of home appliances. In this work, an average estimation accuracy of approximately 87.1% as well as a localization accuracy within 1.4 m, was achieved by utilizing three power meters.…”

Section: Introductionmentioning

confidence: 99%

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Effective Trilateration-based Indoor Localization Method Utilizing Active Control of Lighting Devices

Moriya

Fujimoto

Arakawa³

et al. 2020

Sensors and Materials

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In this paper, we propose a novel trilateration-based indoor localization method utilizing active control of lighting devices. The proposed method estimates the position of a target for measurement by utilizing a trilateration method based on the distances between the target and lighting devices calculated from the illuminance value obtained by active control. The active control involves turning on lighting devices newly installed in the target area one by one. Our method has three key ideas. The first is to remove the effect of light sources other than the lighting equipment used for localization by utilizing only the difference between the illuminance values obtained before and after turning on each lighting device. The second is to remove the influence of a human shadow by utilizing three lighting devices used for localization selected from four lighting devices newly installed in the target area. The third is to accurately estimate the position of a target by introducing a distance-illuminance model that can calculate the distance between an illuminance sensor and a lighting device accurately. To show the effectiveness of the proposed method, we conducted evaluation experiments. It was found that the proposed method can estimate the position of a target with approximately 1 m error on average.

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“…We consider that the discomfort to the user may be reduced by performing the active control of lighting devices while avoiding the specific activity recognized by certain methods. (8,9)…”

Section: Limitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective Trilateration-based Indoor Localization Method Utilizing Active Control of Lighting Devices

Moriya

Fujimoto

Arakawa³

et al. 2020

Sensors and Materials

Self Cite

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“…Binary sensors have been used in a recent HAR study conducted by [26] to recognize nine ADLs, such as cleaning, cooking and sleeping, performed by four smart home inhabitants. The sensors deployed included motion detectors integrated within, or attached to, smart appliances.…”

Section: Human Activity Recognition (Har)mentioning

confidence: 99%

Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Kamoen

Nugent

Zhang

et al. 2019

Sensors

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In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.

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“…A recently conducted study (Moriya et al 2017) used motion detectors attached to, or integrated within, various smart appliances to recognize activities of daily living. These appliances also included ON/OFF states for ceiling lights, IH cooking heaters, TV, PC, and cleaning appliances e.g.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition

Zhang¹,

Zhang

et al. 2019

J Ambient Intell Human Comput

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Sensor-based activity recognition involves the automatic recognition of a user's activity in a smart environment using computational methods. The use of wearable devices and video-based approaches have attracted considerable interest in ubiquitous computing. Nevertheless, these methods have limitations such as issues with privacy invasion, ethics, comfort and obtrusiveness. Environmental sensors are an increasingly promising consideration in the ubiquitous computing domain for long-term monitoring, as these devices are non-invasive to inhabitants, yet certain challenges remain with activity recognition in sensorised environments, for example, addressing the challenge of intraclass variation between activities and reasoning from low-level uncertain information. In an effort to address these challenges, this paper proposes and evaluates the performance of a Radial Basis Function Neural Network approach for activity recognition with environmental sensors. The model is trained using the Localized Generalization Error and focuses on the generalization ability by considering both the training error and stochastic sensitivity measure. This measures the network output fluctuation with respect to the minor perturbation of input, to address the tolerance of the low-level uncertain sensor data. This approach is compared with three benchmark Neural Network approaches, including a popular deep learning approach using an Autoencoder, and it is evaluated with a simulated dataset as well as a number of publicly available datasets. The proposed method has shown advantages over the other models for all four evaluated datasets. This paper provides insights into the importance of model generalization abilities and an initial analysis of the limitation of deep Neural Networks with respect to sensor-based activity recognition.

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Daily living activity recognition with ECHONET Lite appliances and motion sensors

Cited by 23 publications

References 12 publications

Effective Trilateration-based Indoor Localization Method Utilizing Active Control of Lighting Devices

Effective Trilateration-based Indoor Localization Method Utilizing Active Control of Lighting Devices

Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition

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