A Location Based Sequence Prediction Algorithm for Determining Next Activity in Smart Home

Marufuzzaman, Mohammad; Reaz, Mamun Bin Ibne; Rahman, Labonnah Farzana; Farayez, Araf

doi:10.25103/jestr.102.19

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…Machine learning algorithm variants include Reinforcement Learning (RL) [10] [11], Neural Networks (NN) [12] [13] [14] [15], Recurrent Neural Networks (RNN) [16], Long Short Term Memory (LSTM) [16] [17], and Support Vector Machines (SVM) [17]. Probabilistic Graphical Models include SPEED algorithm variants (sequence prediction via enhanced episode discovery) [18] [19] [20] and CRAFFT algorithm variants (current activity and features to predict next features / Bayesian network) [21]. Lastly, Statistical and Trend Analysis which includes Facebook's Prophet, ARIMA, and SAR-IMA [22] [23].…”

Section: Behaviour Prediction Algorithmsmentioning

confidence: 99%

Computational methods for predicting human behaviour in smart environments

Dunne

Matthews

Vega

et al. 2023

AIS

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This systematic literature review presents the computational methods of human behaviour prediction research from Pentland and Liu’s seminal 1999 paper on human behaviour prediction to the latest research to date. The PRISMA framework for systematic reviews was used as the review methodology to structure this information aggregation. This review provides a high-level summary of the field with key areas identified for new research. The results show that there are frequently used datasets for training predictive models: MavHome, MavLab, LIARA, CASAS, PlaceLab, and REDD. Accuracies in the range of 43.9% to 100% for predictions of varying complexity. Common data structures for modelling behavioural data: Vectors, tables, trees, Markov models, and graphs. Algorithms that fall into three distinct categories: Machine Learning (NN, RL, LSTM), Probabilistic Graphical Models (namely Bayesian and Markov variants), and Statistical and Trend Analysis (ARIMA, Prophet). Additionally, we document other notably useful algorithms that fall outside of these three main categories including Jaro-Winkler and Levenshtein distances. Opportunities identified for further research include the use of audio as the data source for behaviour prediction methods, and applying times-series prediction machine learning algorithms (RNN, LSTM) to the smart home problem space.

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Section: Behaviour Prediction Algorithmsmentioning

confidence: 99%

Computational methods for predicting human behaviour in smart environments

Dunne

Matthews

Vega

et al. 2023

AIS

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“…In the case of the MavLab dataset, the room number is also included in the newly generated sensor codes. Here, the approach developed by Marufuzzaman et al [21] is used which uses the following equation:…”

Section: ) Stepmentioning

confidence: 99%

“…An average prediction accuracy of 88.3% is achieved which surpasses the performance of LeZi Update and ALZ algorithm. Further development of SPEED is performed by Marufuzzaman et al who included the time and location components to formulate Modified-SPEED or M-SPEED [20], [21]. Addition of time component can reduce redundancy in data caused by corrupted sensors.…”

Section: Introductionmentioning

confidence: 99%

SPADE: Activity Prediction in Smart Homes Using Prefix Tree Based Context Generation

2019

Self Cite

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An automated smart home system is a key contributor to user assistance technology in modern civilization. Crucial merit of such a system is its ability to train itself through recorded data and recognize patterns in resident behaviors. Lack of sufficient prediction accuracy, exponential memory consumption, and extensive runtime prevent many of the current activity prediction approaches from being seamlessly integrated into consumer residences. This research introduces a sequence prediction algorithm which uses a prefix tree-based data model in order to learn and predict user actions. The algorithm applies episode discovery to detect correlated sensor events and learns the activities using a lossless data compression technique. This process assigns a probability of occurrence to sensor events and uses these probabilities to detect patterns in resident behavior. A complexity analysis of the algorithm is done to prove its efficiency in terms of memory usage and runtime. Using the presented technique, predictions are performed on popular datasets and contrasted with existing algorithms. The proposed algorithm achieves an 8.22% improvement in prediction accuracy over its predecessors, along with 66.69% better memory efficiency and 37% faster runtime.

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“…The resultant M-SPEED (Modified-SPEED) can eliminate false episode detection and demonstrate a higher prediction accuracy. Further enhancements are added to this process by introduction of the location component in order to specifically carry out particular actions based on individual's location in the house [10]. A more recent algorithm SPADE (Sequence Prediction via All Discoverable Episodes) modifies the M-SPEED in tree generation phase to further improve the accuracy and runtime [11].…”

Section: Introductionmentioning

confidence: 99%

Improved data storage performance based modified-SPEED algorithm

Reaz

Farayez

2021

Jurnal Informatika

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With the rising demand for smart devices and smart home systems, automation and activity prediction have become a vital aspect of people's everyday lives. Researchers have focused on developing approaches which detect patterns in user activities and used them to predict future actions. One such system is Modified Sequence Prediction via Enhanced Episode Discovery (M-SPEED), that uses spatiotemporal data of activities of daily lives to analyze user behaviors. But the low accuracy of this algorithm can be a limiting factor in efficient activity prediction. Also, the computational overhead of run time and memory causes this algorithm to show poor performance in case of large datasets. This research focuses on modifying the M-SPEED algorithm to improve its capability to run on larger dataset while at the same time improving run time. The accuracy is also improved to make it more effective in real-world applications. Proof of algorithm efficiency is provided to ensure system validity, and simulation is carried out on real-life data. The results demonstrate a 66.69% improvement in cumulative memory efficiency, 37% faster run time, and 8.22% better accuracy confirming the effectiveness of the proposal.

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A Location Based Sequence Prediction Algorithm for Determining Next Activity in Smart Home

Cited by 15 publications

References 14 publications

Computational methods for predicting human behaviour in smart environments

Computational methods for predicting human behaviour in smart environments

SPADE: Activity Prediction in Smart Homes Using Prefix Tree Based Context Generation

Improved data storage performance based modified-SPEED algorithm

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