IoT inspired smart environment for personal healthcare in gym

Ahanger, Tariq Ahamed

doi:10.1007/s00521-022-07488-8

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…Smart homes include digital sensing technologies and communication devices that can seamlessly and intelligently communicate with one another and with the outside world via the internet and mobile networks [ 1 , 4 ]. Examples of smart home devices include ambient devices, cameras, wearable sensors, fall prediction systems, fall detection medical alert systems [ 5 , 6 ], smart fridges, smart vacuum cleaners, smart washing machines, smart dryers, smart doorbells, smart toothbrushes, and smart home gym equipment [ 4 , 7 , 8 ]. The “smartness” of these devices and systems used in smart homes relates to their automated functions, such as context awareness, voice activation, event detection, activity monitoring, and data-driven artificial intelligence processing features that can adapt to the conditions of the homes and lifestyles of the home occupant [ 4 ].…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…Read structures, which are sometimes tough to interpret by human medical professionals, are encouraged to be used as a result of this. Figure 10 is a representation of the proposed architecture for the CNN, which, when implemented, would result in the classification of lung tumour stages based on the data obtained [ 44 ]. Table 4 shows the tumor stages.…”

Section: Proposed Methodologymentioning

confidence: 99%

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things

Joshi

Pandit²,

Shukla

et al. 2022

Computational Intelligence and Neuroscience

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The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients’ courses of treatment, has become increasingly widespread in recent years in the field of healthcare. On the other hand, the number of PET scans that are being performed is rising, and radiologists are getting significantly overworked as a result. As a direct result of this, a novel approach that goes by the name “computer-aided diagnostics” is now being investigated as a potential method for reducing the tremendous workloads. A Smart Lung Tumor Detector and Stage Classifier (SLD-SC) is presented in this study as a hybrid technique for PET scans. This detector can identify the stage of a lung tumour. Following the development of the modified LSTM for the detection of lung tumours, the proposed SLD-SC went on to develop a Multilayer Convolutional Neural Network (M-CNN) for the classification of the various stages of lung cancer. This network was then modelled and validated utilising standard benchmark images. The suggested SLD-SC is now being evaluated on lung cancer pictures taken from patients with the disease. We observed that our recommended method gave good results when compared to other tactics that are currently being used in the literature. These findings were outstanding in terms of the performance metrics accuracy, recall, and precision that were assessed. As can be shown by the much better outcomes that were achieved with each of the test images that were used, our proposed method excels its rivals in a variety of respects. In addition to this, it achieves an average accuracy of 97 percent in the categorization of lung tumours, which is much higher than the accuracy achieved by the other approaches.

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“…If it is determined that the signal is in fact related to music, the label will be categorized as one of the following instruments: cello, clarinet, fute, guitar, organ, piano, saxophone, trumpet, violin, and band. If the testing characteristic is found to be a speech, the voice will be classifed as either male or female once again [41].…”

Section: Retrievalmentioning

confidence: 99%

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Samha

Alshammri

Nuagah

et al. 2023

Computational Intelligence and Neuroscience

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Audio classification and retrieval has been recognized as a fascinating field of endeavor for as long as it has existed due to the topic of identifying and choosing the most useful audio attributes. The categorization of audio files is significant not only in the area of multimedia applications but also in the disciplines of medicine, sound analysis, intelligent homes and cities, urban informatics, entertainment, and surveillance. This study introduces a new algorithm called the modified bacterial foraging optimization algorithm (MBFOA), which is based on a method that retrieves and classifies audio data. The goal of this algorithm is to reduce the computational complexity of existing techniques. Along with the combination of the peak estimated signal, the enhanced mel-frequency cepstral coefficient (EMFCC) and the enhanced power normalized cepstral coefficients (EPNCC) are used. These are then optimized using the fitness function utilizing MBFOA. The probabilistic neural network is used to differentiate between a music signal and a spoken signal from an audio source (PNN). It is next necessary to extract and list the characteristics that correspond to the class that was arrived at as a consequence of the categorization. When compared to other approaches that are somewhat similar, MBFOA demonstrates superior levels of sensitivity, specificity, and accuracy.

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IoT inspired smart environment for personal healthcare in gym

Cited by 8 publications

References 33 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

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

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