Indirect Recognition of Predefined Human Activities

Gorjani, Ojan Majidzadeh; Proto, Antonino; Vaňuš, Jan; Bilík, Petr

doi:10.3390/s20174829

Cited by 8 publications

(17 citation statements)

References 66 publications

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“…Other algorithms such as artificial neural networks (ANNs), convolutional neural networks, and hidden Markov models were only applied once. Compared with Bayesian or neural networks, which require and use a large amount of training data sets (eg, the study by Gorjani et al [ 35 ], which used approximately 300,000 data points), support vector machine used a small number of available training sets (eg, the study by Li et al [ 39 ], which used <200 data points).…”

Section: Resultsmentioning

confidence: 99%

“…The included studies were conducted in various settings. In total, 40% (8/20) were conducted in a laboratory setting [ 32 , 35 , 37 , 39 - 41 , 48 , 49 ], 25% (5/20) were conducted in a home setting [ 3 , 32 , 33 , 43 , 51 ], and 10% (2/20) were conducted in a hospital setting [ 42 , 47 ]. Laboratory setting means that the study was conducted in an environment where researchers installed smart home sensors in a laboratory for data collection instead of in a real home setting, which has basic rooms and equipment for daily living.…”

Section: Resultsmentioning

confidence: 99%

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Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Oyibo¹,

Wang²,

Morita³

2023

J Med Internet Res

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Background Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups. Objective This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities. Methods Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts. Results This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naïve Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome. Conclusions Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Oyibo¹,

Wang²,

Morita³

2023

J Med Internet Res

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show abstract

“…This allows better training of larger models in a given time. With direct comparison with our previous study [ 13 ], which used two hidden layers, the cross-validation accuracy was almost identical (within margins of error). However, due to higher and more stable data acquisition rates, the scoring accuracy was significantly improved.…”

Section: Discussionmentioning

confidence: 55%

“…A total of 84 models were developed to examine the recognition accuracy of these activity classes. The models used for cross-validation (42 models) mostly showed accuracy levels above 99%, which is considerably more accurate than similar implementations and our previous study [ 13 ]. The relaxing activity showed mostly 100% recognition accuracy levels, and other activities cross-validated to accuracy levels above 98.86%.…”

Section: Discussionmentioning

confidence: 62%

“…Based on the received data and the use of artificial neural networks, the local host computer can recognize few human activity classes. In our previous works [ 13 ], IBM SPSS Modeler and IBM SPSS statistics were used to implement feed-forward neural networks and logistical regression. IBM SPSS Modeler and IBM SPSS statistics are software tools that are commonly used to implement statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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Human Activity Classification Using Multilayer Perceptron

Gorjani

Byrtus

Dohnal

et al. 2021

Sensors

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The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

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Review on Human Action Recognition in Smart Living: Multimodality, Real-time Processing, Interoperability, Resource-Constrained Processing, and Sensing Technology

Diraco¹,

Rescio²,

Siciliano³

et al. 2023

Preprint

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Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains: Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to explore further and advance the field of human action recognition in smart living.

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Indirect Recognition of Predefined Human Activities

Cited by 8 publications

References 66 publications

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Human Activity Classification Using Multilayer Perceptron

Review on Human Action Recognition in Smart Living: Multimodality, Real-time Processing, Interoperability, Resource-Constrained Processing, and Sensing Technology

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