Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Reyes-Campos, Josimar; Alor-Hernández, Giner; Machorro-Cano, Isaac; Olmedo-Aguirre, José Oscar; Sánchez-Cervantes, José Luis; Rodríguez-Mazahua, Lisbeth

doi:10.3390/math9030219

Cited by 29 publications

(19 citation statements)

References 44 publications

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“…Hitherto, HAR has been widely used in numerous applications, like gesture recognition gait analysis, humancomputer interaction, home behavior analysis, personal health system, video surveillance, and antiterrorism monitoring [9][10][11][12][13][14][15][16]. It has the ability to learn in advance from raw data around human activities.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model

Rahman

Ullah

Salam

et al. 2022

Journal of Healthcare Engineering

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According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function, paralysis of body parts, and severe back pain for which a physiotherapist employs many therapies to restore the mobility needs of everyday life. This research article presents an automated approach to detect different therapy exercises performed by stroke patients during rehabilitation. The detection of rehabilitation exercise is a complex area of human activity recognition (HAR). Due to numerous achievements and increasing popularity of deep learning (DL) techniques, in this research article a DL model that combines convolutional neural network (CNN) and long short-term memory (LSTM) is proposed and is named as 3-Layer CNN-LSTM model. The dataset is collected through RGB (red, green, and blue) camera under the supervision of a physiotherapist, which is resized in the preprocessing stage. The 3-layer CNN-LSTM model takes preprocessed data at the convolutional layer. The convolutional layer extracts useful features from input data. The extracted features are then processed by adjusting weights through fully connected (FC) layers. The FC layers are followed by the LSTM layer. The LSTM layer further processes this data to learn its spatial and temporal dynamics. For comparison, we trained CNN model over the prescribed dataset and achieved 89.9% accuracy. The conducted experimental examination shows that the 3-Layer CNN-LSTM outperforms CNN and KNN algorithm and achieved 91.3% accuracy.

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Section: Introductionmentioning

confidence: 99%

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model

Rahman

Ullah

Salam

et al. 2022

Journal of Healthcare Engineering

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“…Recent applications in remote healthcare have reaffirmed the above approaches, proposing innovative solutions in this regard. The authors in [23] presented a smart home control platform that offered fully customized automatic control schemes and performed the analysis of historical records of the use of home automation devices, in order to detect residents' behavior patterns through IoT and machine learning, improving the comfort schemes of domestic systems.…”

Section: Related Workmentioning

confidence: 99%

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

et al. 2021

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Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

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“…In recent years, various advanced computational approaches have been applied in different fields such as chemical engineering [37][38][39][40][41][42][43][44], control and electrical engineering [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59], pharmacy and medical science [60][61][62][63][64][65][66][67], industrial engineering [68,69], civil engineering [70][71][72][73], economic and business sciences, [74][75][76][77][78], mechanical engineering [79][80][81][82][83][84][85], energy engineering…”

Section: Mlp Neural Networkmentioning

confidence: 99%

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

et al. 2021

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This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

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Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Cited by 29 publications

References 44 publications

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

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