LSTM and HMM Comparison for Home Activity Anomaly Detection

Poh, Soon-Chang; Tan, Yi-Fei; Guo, Xianping; Cheong, Soon-Nyean; Ooi, Chee‐Pun; Tan, Wooi-Haw

doi:10.1109/itnec.2019.8729168

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…Overall, the LSTM model performed better than similar models that have been used in research, including in [18,19]. The simulation-based motion data took less time to generate, with almost no cost involved.…”

Section: Lstm Model Testing Resultsmentioning

confidence: 90%

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On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

et al. 2023

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Road hazards are one of the significant sources of fatalities in road accidents. The accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a need for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. This work presents a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones on board, we aim to leverage this to detect road hazards more flexibly, cost-effectively, and efficiently. This paper proposes a cloud-based deep-learning road hazard detection model based on a long short-term memory (LSTM) network to detect different types of road hazards from the motion data. To address the issue of large data requests for deep learning, this paper proposes to leverage both simulation data and experimental data for the learning process. To address the issue of misdetections from an individual smartphone, we propose a cloud-based fusion approach to further improve detection accuracy. The proposed approaches are validated by experimental tests, and the results demonstrate the effectiveness of road hazard detection.

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Section: Lstm Model Testing Resultsmentioning

confidence: 90%

“…LSTM outperformed ARIMA model. Poh et al (2019) [19] compared LSTM and a hidden Markov model (HMM) to detect anomilies in daily activity sequences. Their results showed that LSTM outperforms the HMM.…”

Section: Introductionmentioning

confidence: 99%

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

et al. 2023

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“…It also has a cell state, which selectively retains or forgets information. The gates in an LSTM, namely the forget gate, input gate, and output gate, control the flow of data through mathematical operations such as elementwise multiplication and addition [52]. Figure 2 presents the base structure of LSTM.…”

Section: Long Short-term Memory (Lstm) Modelmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

Rahman,

Gupta,

Bhatt

et al. 2024

Future Internet

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Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.

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“…We proposed segmenting the sequence into smaller segments before passing them to RNN. We have shown that segmenting sequences before passing them to RNN resulted in excellent results in terms of accuracy on anomaly detection task [26]. In the data processing step, we segment the sequence of each activity type by using sliding window.…”

Section: Data Processingmentioning

confidence: 99%

Time series activity classification using gated recurrent units

Tan

Guo

Poh

2021

IJECE

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<span>The population of elderly is growing and is projected to outnumber the youth in the future. Many researches on elderly assisted living technology were carried out. One of the focus areas is activity monitoring of the elderly. AReM dataset is a time series activity recognition dataset for seven different types of activities, which are bending 1, bending 2, cycling, lying, sitting, standing and walking. In the original paper, the author used a many-to-many Recurrent Neural Network for activity recognition. Here, we introduced a time series classification method where Gated Recurrent Units with many-to-one architecture were used for activity classification. The experimental results obtained showed an excellent accuracy of 97.14%.</span>

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LSTM and HMM Comparison for Home Activity Anomaly Detection

Cited by 13 publications

References 6 publications

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

Time series activity classification using gated recurrent units

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