Activity recognition using semi-Markov models on real world smart home datasets

Kasteren, T.L.M. van; Englebienne, Gwenn; Kröse, B.J.A.

doi:10.3233/ais-2010-0070

Cited by 104 publications

(57 citation statements)

References 18 publications

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“…In addition, many machine-learning methods on smart homes have been proposed [27], such as process mining [28], active learning and dynamic K-means [29]. Van Kasteren et al recorded 25 days' activities data in the home of a 26-year-old male by using a wireless sensor network [30]. 14 binary sensors were used to collect 7 types of activity, including taking a shower and preparing dinner.…”

Section: Related Workmentioning

confidence: 99%

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Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Wang

et al. 2020

Sensors

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Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people's lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people's activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively. which can be used to underpin a multitude of services such as personal health management [2], elderly care services [3], smart home services [4,5], to name but a few. However, it is a complicated process with many unsolved challenges such as multiple occupancy, interleaved activities, incomplete sensor data and differences in inter-and intra-inhabitant behaviors. As a result, due to the diversity of its data sources (smart home, smart wearable devices, mobile phones sensors and cameras), various solutions have been proposed to solve this problem [6].Some research studies in the area of activity recognition have been based on image data collected by cameras [7][8][9][10]. Limited by computational complexity and privacy issues, this type of activity recognition is mainly used in the field of public safety and human-computer interaction. As an alternative to image-based data, some research has been based on data collected from wearable devices and smartphones [11][12][13]. In this paper, we focus on home-based activity recognition, which is mainly used in elderly care service or smart home service provision. Binary data were collected through binary sensors embedded ...

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Section: Related Workmentioning

confidence: 99%

“…Four binary datasets including OrdonezA, OrdonezB [31], Ulster [14], and activities of daily living data from van Kasteren (vanKasterenADL) [30] have been used to evaluate the effectiveness of the proposed method. Various sensors were used to collect data, including pressure sensors, contact sensors and passive infrared sensors.…”

Section: Datasetsmentioning

confidence: 99%

Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Wang

et al. 2020

Sensors

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“…Semi-Markov Models (HSMM) have the advantage of modelling time distribution for the different behavioral states. HSMM have proven to be valuable for similar segmentation tasks in ubiquitous computing [28,29]. In a Hidden semi-Markov model (HSMM) clustering is performed into hidden states.…”

Section: Hidden Semi-markov Modelsmentioning

confidence: 99%

“…In the present study we limited the number of metrics to facilitate a standardized comparison with the cut-points approach and to facilitate interpretation. The use of the z-angle for sustained inactivity detection in the cut-points approach does not undermine the standardized comparison, because the HSMM model also uses this information: When calculating the magnitude of acceleration that is used as input for the HSMM model, values are 29 replaced by zero when the z-angle is constant for a five minutes. The use of different distributions to represent the data in the HSMM model could be investigated, such as a lognormal distribution for the acceleration metric.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Segmenting accelerometer data from daily life with unsupervised machine learning

Kuppevelt

Heywood²,

Hamer

et al. 2018

Preprint

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Purpose: Accelerometers are increasingly used to obtain valuable descriptors of physical activity for health research. The cut-points approach to segment accelerometer data is widely used in physical activity research but requires resource expensive calibration studies and does not make it easy to explore the information that can be gained for a variety of raw data metrics. To address these limitations, we present a data-driven approach for segmenting and clustering the accelerometer data using unsupervised machine learning. Methods:The data used came from five hundred fourteen-year-old participants from the Millennium cohort study who wore an accelerometer (GENEActiv) on their wrist on one weekday and one weekend day. A Hidden Semi-Markov Model (HSMM), configured to identify a maximum of ten behavioral states from five second averaged acceleration with and without addition of x, y, and z-angles, was used for segmenting and clustering of the data. A cut-points approach was used as comparison.Results: Time spent in behavioral states with or without angle metrics constituted eight and five principal components to reach 95% explained variance, respectively; in comparison four components were identified with the cut-points approach. In the HSMM with acceleration and angle as input, the distributions for acceleration in the states showed similar groupings as the cutpoints categories, while more variety was seen in the distribution of angles. Conclusion:Our unsupervised classification approach learns a construct of human behavior based on the data it observes, without the need for resource expensive calibration studies, has the ability to combine multiple data metrics, and offers a higher dimensional description of physical behavior. States are interpretable from the distributions of observations and by their duration.3

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“…Different types of sensors have been applied to the task of activity recognition [4], [5]. Kasteren et al [6] adopt a set of simple sensors, i.e. , pressure, contact, and motion sensors, to recognize daily activities of people in a smart home.…”

Section: Introductionmentioning

confidence: 99%

Latent Hierarchical Model for Activity Recognition

Englebienne

Lou

et al. 2015

IEEE Trans. Robot.

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Abstract-We present a novel hierarchical model for human activity recognition. In contrast to approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a Structured Support Vector Machine (Structured-SVM). A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.

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Activity recognition using semi-Markov models on real world smart home datasets

Cited by 104 publications

References 18 publications

Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Segmenting accelerometer data from daily life with unsupervised machine learning

Latent Hierarchical Model for Activity Recognition

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