A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

Section: The Role Of Artificial Intelligence (Ai) On Smart Tailored Ementioning

confidence: 99%

“…The authors achieved a recognition precision of 85.0% and prediction accuracy of 78.3%. Their solution showed a stronger performance and accuracy than the classical naïve Bayes method [124].…”

Section: The Role Of Artificial Intelligence (Ai) On Smart Tailored Ementioning

confidence: 99%

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Rodrigues

Postolache

Cercas

2020

“…Sungjoon et al used a hybrid network framework to predict various daily activities [19]. A recursive neural network (RNN) [20][21][22][23][24] and LSTM network [13] were used in daily activity forecasting in an in-depth study. Although these neural networks improved the forecast performance to some extent, they only trained the category forecast model of daily activities and ignored the time information of the daily activities themselves.…”

Section: Related Workmentioning

confidence: 99%

“…So far, category forecasts and occurrence time forecasts of daily activity have been explored separately rather than as a whole [11][12][13][14]. Daily activity forecasts are usually separated into several independent sub-tasks, and then the results of sub-tasks are combined.…”

Section: Introductionmentioning

confidence: 99%

A Multi-task Learning Model for Daily Activity Forecast in Smart Home

Yang

Gong

Liu

et al. 2020

Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.

“…Over the past few years, with the development of 5G and the advancement of Internet Protocol Version 6 (IPv6), the Internet of Things (IoT) paradigm has been greatly developed [1]. One particular instance of this is in the domain of smart homes where sensing devices have gradually entered into people's lives.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2020

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 ...