Multi-label classification based ensemble learning for human activity recognition in smart home

Jethanandani, Manan; Sharma, Abhishek; Perumal, Thinagaran; Chang, Jieh-Ren

doi:10.1016/j.iot.2020.100324

Cited by 42 publications

(17 citation statements)

References 15 publications

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“…All proposed models were trained using a whole month’s data. Similar attempts to address the problem trained a different model for each day and averaged the individual results [ 29 ]. The average F1 score and Hamming loss for each classifier and house can be seen in Table 4 and Table 5 , respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The results were consistent and no major deviations were observed between different days. Furthermore, the results of

using MLP are compared with the results obtained on the same dataset by [ 29 ] ( Table 10 ). The comparison is based on the only common metric, Hamming loss.…”

Section: Resultsmentioning

confidence: 99%

“…In our work, several state-of-the-art (

, classifier chain, binary relevance) MLC algorithms are evaluated. Algorithms were chosen based on previous benchmarks [ 29 , 35 ]. All algorithms were implemented using Python and Scikit-Learn [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…Multi-label classification, while extensively used on multiple domains, such as text [ 26 ], video [ 27 ], and image [ 28 ] classification, etc., have only recently drawn the attention of researchers to the activity recognition field. A comparative experimental study between two well-known MLC algorithms [ 29 ] proved that MLC can address concurrent activity recognition effectively. Interaction between actions performed by different people can be identified by transforming the multi-resident activity recognition problem into an MLC problem.…”

Section: Related Workmentioning

confidence: 99%

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Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms

Lentzas

Dalagdi

Vrakas

2022

Sensors

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As the world’s population is aging, and since access to ambient sensors has become easier over the past years, activity recognition in smart home installations has gained increased scientific interest. The majority of published papers in the literature focus on single-resident activity recognition. While this is an important area, especially when focusing on elderly people living alone, multi-resident activity recognition has potentially more applications in smart homes. Activity recognition for multiple residents acting concurrently can be treated as a multilabel classification problem (MLC). In this study, an experimental comparison between different MLC algorithms is attempted. Three different techniques were implemented: RAkELd, classifier chains, and binary relevance. These methods are evaluated using the ARAS and CASAS public datasets. Results obtained from experiments have shown that using MLC can recognize activities performed by multiple people with high accuracy. While RAkELd had the best performance, the rest of the methods had on-par results.

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Section: Resultsmentioning

confidence: 99%

“…The results were consistent and no major deviations were observed between different days. Furthermore, the results of

using MLP are compared with the results obtained on the same dataset by [ 29 ] ( Table 10 ). The comparison is based on the only common metric, Hamming loss.…”

Section: Resultsmentioning

confidence: 99%

“…In our work, several state-of-the-art (

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms

Lentzas

Dalagdi

Vrakas

2022

Sensors

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show abstract

“…Other papers focus on the problem of the recognition of multi-resident activities in a smart-home infrastructure [40,41] (using the CASAS dataset with 60 sensors and 2 residents), [42] (using the ARAS dataset), and they can have as a consequence the possibility of counting the number of people. However, given their main goal, the number of sensors is typically very high, and the number of residents is limited to two persons.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Resident Number Estimation Method for Smart Homes

Masciadri

Lin

Comai

et al. 2022

Sensors

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Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.

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Human Activity Recognition for Multi-label Classification in Smart Homes Using Ensemble Methods

Kasubi

Huchaiah

2021

Communications in Computer and Information Science

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Multi-label classification based ensemble learning for human activity recognition in smart home

Cited by 42 publications

References 15 publications

Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms

Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms

A Multi-Resident Number Estimation Method for Smart Homes

Human Activity Recognition for Multi-label Classification in Smart Homes Using Ensemble Methods

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