Human Activity Recognition based on smart home environment and their applications, challenges

Kalimuthu, Sivakumar; Perumal, Thinagaran; Yaakob, Razali; Marlisah, Erzam; Babangida, Lawal

doi:10.1109/icacite51222.2021.9404753

Cited by 9 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, cheap sensors do not have such disadvantages and became more practical methods used in collecting information. These tools have various types of sensors, such as wearable, ambient, and object-tagged sensors [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. After collecting data, we need to choose an adequate model to capture the hidden patterns of human activities.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with an HMM-Based Generative Model

Manouchehri

Bouguila

2023

Sensors

View full text Add to dashboard Cite

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

show abstract

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with an HMM-Based Generative Model

Manouchehri

Bouguila

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Although the capabilities of such devices are improving and their performance is rising, HAR is still a complicated task. Each activity, by its nature, is difficult to recognise because it can be influenced by a variety of situations, and even the same person can execute the same activity in two different ways depending on the circumstances [14].…”

Section: Introductionmentioning

confidence: 99%

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

D'Arco

Wang

Zheng

2023

Neural Comput & Applic

View full text Add to dashboard Cite

Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest degree of recognition, closely followed by the Spinning Bike class, but fitness activities were recognised at a higher rate than ambulation activities. A comparative analysis was carried out both to determine the impact that pre-processing had on the proposed core architecture and to compare the proposed solution with existing state-of-the-art solutions. The results, in addition to demonstrating how deep learning solutions outperformed those of shallow machine learning, showed that in our solution the use of data pre-processing increased performance by about 2%, optimising the handling of the imbalanced dataset and allowing a relatively simple network to outperform more complex networks, reducing the computational impact required for such applications.

show abstract

“…HAR is a process that uses a series of observations of human activity and its surrounding environment to infer the current behaviours and ways of human movement. Recently, research on HAR has drawn more academic and commercial interest because of its numerous applications in different fields, such as smart home [ 1 ] (managing different appliances based on human actions for minimizing energy consumption), healthcare [ 2 ] (monitoring the health of ageing citizens and disabled people), security [ 3 ] (bio-metric or face recognition), and video gaming, etc. Traditional HAR approaches use several sensing technologies: computer vision [ 4 ], wearable sensors [ 5 ], and radars [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI

Islam

Jannat

Hossain

et al. 2022

Sensors

View full text Add to dashboard Cite

Human activity recognition (HAR) has emerged as a significant area of research due to its numerous possible applications, including ambient assisted living, healthcare, abnormal behaviour detection, etc. Recently, HAR using WiFi channel state information (CSI) has become a predominant and unique approach in indoor environments compared to others (i.e., sensor and vision) due to its privacy-preserving qualities, thereby eliminating the need to carry additional devices and providing flexibility of capture motions in both line-of-sight (LOS) and non-line-of-sight (NLOS) settings. Existing deep learning (DL)-based HAR approaches usually extract either temporal or spatial features and lack adequate means to integrate and utilize the two simultaneously, making it challenging to recognize different activities accurately. Motivated by this, we propose a novel DL-based model named spatio-temporal convolution with nested long short-term memory (STC-NLSTMNet), with the ability to extract spatial and temporal features concurrently and automatically recognize human activity with very high accuracy. The proposed STC-NLSTMNet model is mainly comprised of depthwise separable convolution (DS-Conv) blocks, feature attention module (FAM) and NLSTM. The DS-Conv blocks extract the spatial features from the CSI signal and add feature attention modules (FAM) to draw attention to the most essential features. These robust features are fed into NLSTM as inputs to explore the hidden intrinsic temporal features in CSI signals. The proposed STC-NLSTMNet model is evaluated using two publicly available datasets: Multi-environment and StanWiFi. The experimental results revealed that the STC-NLSTMNet model achieved activity recognition accuracies of 98.20% and 99.88% on Multi-environment and StanWiFi datasets, respectively. Its activity recognition performance is also compared with other existing approaches and our proposed STC-NLSTMNet model significantly improves the activity recognition accuracies by 4% and 1.88%, respectively, compared to the best existing method.

show abstract

Human Activity Recognition based on smart home environment and their applications, challenges

Cited by 9 publications

References 24 publications

Human Activity Recognition with an HMM-Based Generative Model

Human Activity Recognition with an HMM-Based Generative Model

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI

Contact Info

Product

Resources

About