Application of Internet of Things and artificial intelligence for smart fitness: A survey

Farrokhi, Alireza; Farahbakhsh, Reza; Rezazadeh, Javad; Minerva, Roberto

doi:10.1016/j.comnet.2021.107859

Cited by 75 publications

(43 citation statements)

References 104 publications

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“…Like other applications of IoT, the advancement in IoT technology for fitness applications has shown tremendous progress in the last few years [14]. Various fitness devices are implanted or attached to the human body to get an insight into various fitness indexes and health status of the sports fitness players in a ubiquitous way, thus helping in health assessment of the humans and fitness maintenance of the sports players [21].…”

Section: Machine Learning Approaches For Fitness Technologymentioning

confidence: 99%

“…In addition, their exists some IoT embedded devices that are useful in ensuring the trainee safety like better workout plan management and notifying the trainees about their stress level. Various approaches have been introduced by the research community relying on the Artificial Intelligence based approaches like DL (Deep Learning), RL (Reinforcement Learning) for various type of decision makings [14,15]. The DL-based approaches are powerful data analytic techniques concerning intelligent decision-making, accurate prediction, and classification in the context of IoT.…”

Section: Introductionmentioning

confidence: 99%

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Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

Ibrahim¹,

Jamil²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging. To overcome these issues, this work proposed a blockchain-based load-balanced task scheduling approach. A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches. The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users' requests and improve resource utilization. The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio. The obtained results confirm the efficiency of the proposed scheduling approach. For investigating the performance of the blockchain, various experiments are performed using the Hyperledger Caliper concerning latency, throughput, resource utilization. The Solo approach has shown an improvement of 32% and 26% in throughput as compared to Raft and Solo-Raft approaches respectively. The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

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Section: Machine Learning Approaches For Fitness Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

Ibrahim¹,

Jamil²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Last, we review the actions users take to protect their privacy. Prior work in this area is vast, so literature surveys have been compiled to support researchers [19,21,23,39,48,80,89,92,94]. In the following sections, we will refer to the individual studies and not to the surveys.…”

Section: Related Workmentioning

confidence: 99%

Are Those Steps Worth Your Privacy?

Velykoivanenko

Niksirat

Zufferey

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.

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“…SST has a strong relationship with Computational Intelligence (CI) [26]. In [24], an AI-based application has been presented to record athlete data and present a goal time detect fitness level to provide a personal best record for each athlete.…”

Section: Related Workmentioning

confidence: 99%

A Decision Tree-Based Smart Fitness Framework in IoT

et al. 2021

Self Cite

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The Internet of Things (IoT) provides opportunities for improving various aspects of human life and enhancing quality of life. It enables monitoring and controlling mechanisms to be more easier than it was in the past. One of the significant objects in people's life is fitness and body activity. Using IoT and its related technologies like RFID (Radio Frequency Identification), AI (Artificial Intelligence), and WSN (Wireless Sensor Networks), can reduce cost and increase accuracy for fitness monitoring. The problem in current smart fitness and fitness monitoring systems is that there is not a complete monitoring and workout plan provider in these scenarios, especially in non-wearable sensors which are most placed on fitness devices. In this paper, an architecture is proposed that can monitor actions which is executed by sensor-embedded on fitness machines. The paper explores the implementation and examination for biceps and triceps actions. For processing data, Decision Tree Classifier (DTC) was used to reduce computational tasks and make decisions about future workout plans for each user.

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Application of Internet of Things and artificial intelligence for smart fitness: A survey

Cited by 75 publications

References 104 publications

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

Are Those Steps Worth Your Privacy?

A Decision Tree-Based Smart Fitness Framework in IoT

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