Evolution of Wireless Standards for Health Monitoring

Saboor, Abdul; Mustafa, A.; Ahmad, Rizwan; Khan, Muneeb A.; Haris, Muhammad; Hameed, R.

doi:10.1109/iemeconx.2019.8877040

Cited by 17 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wearable sensors are worn or attached to various parts of the body to monitor vitals and gait parameters. Therefore, wearables are frequently considered as the the most suitable technology for the healthcare, security, sports, and fitness applications [30]- [32]. In gait analysis, accelerometers [33], [34], gyroscopes [35], Inertial Measurement Units (IMUs) [36] and force sensors [37] are widely used to measure gait characteristics [38].…”

Section: Introductionmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

Self Cite

View full text Add to dashboard Cite

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, they provide better privacy than non-wearable systems. However, wearable devices have limited lifetime processing power [ 40 , 41 ]. Furthermore, the wearable’s data need further processing using statistical or ML algorithms for decision making, as shown in Figure 1 .…”

Section: Fall Detection and Prevention Systemsmentioning

confidence: 99%

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Usmani

Saboor

Haris

et al. 2021

Sensors

Self Cite

131

View full text Add to dashboard Cite

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

show abstract

“…The ESP32 is actually a microcontroller chip. For ESP32 there are some low-cost modules also available [5]. The ESP32 is updated version of the ESP8266.…”

Section: Esp 32 Sta and Ap Modesmentioning

confidence: 99%

Monitoring health parameter of remotely located patient

Pochhi¹,

Thakre²,

Gurve³

2021

International Journal of Computational and Electronic Aspects in Engineering

View full text Add to dashboard Cite

As we know in this pandemic situation social distancing is key to be safe. So, we have to keep safe distance in every situation whether it's working site or in providing service to people. For this situation most important service required by the people is medical. So, we have to style a module which help hospitals to provide implementation to this problem. By keeping this situation in mind project for hospital is going to design.Basically, in this project we are going to measure and monitor the heath parameter of patient like temperature, heartbeat, blood pressure, blood sugar level etc. Those sensed value will be either send directly to controller (Arduino) or will be added to GUI and then send to controller. Then this value will be sent over WIFI network and store in database so that those value can be access whenever patient has appointment by doctor's side system. By implementing this project no Doctor or patient will have direct interaction, to avoid spreading of dangerous virus. Advantage of this system is that Dr. can access patient health parameter at any time and can medicate his patient.

show abstract

Evolution of Wireless Standards for Health Monitoring

Cited by 17 publications

References 15 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Monitoring health parameter of remotely located patient

Contact Info

Product

Resources

About