Non Invasive Skin Hydration Level Detection Using Machine Learning

Liaqat, Sidrah; Dashtipour, Kia; Arshad, Kamran; Ramzan, Naeem

doi:10.3390/electronics9071086

Cited by 33 publications

(29 citation statements)

References 26 publications

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“…Extensive research in the current literature shows that machine learning has been used in different fields such as sentiment analysis Dashtipour et al (2016a) , Dashtipour et al (2016b) , Dashtipour et al (2017a ), Dashtipour et al (2017b) , Gogate et al (2017b) , Dashtipour et al (2017c) , Shiva et al (2017) , Gogate et al (2017a) , Gogate et al (2018) , Dashtipour et al (2018) , Dashtipour et al (2019) , cyber-security Adeel et al (2019a) ; Jiang et al (2019) , Gogate et al (2019b) , Gogate et al (2019a) , Adeel et al (2019b) , Ozturk et al (2019) , Jiang et al (2020) , Dashtipour et al (2020) ; speech enhancement Gogate et al (2020a) , Gogate et al (2020b) , Liaqat et al (2020) , Taylor et al (2020) , Liaqat et al (2020) , Adeel et al (2020) , Guellil et al (2021) , Hussain et al (2021) , Dashtipour et al (2021) , hand-written recognition Ahmed et al (2021) , and posture detection; Liaqat et al (2021) , etc., However, research has not been carried out to detect sentiment polarity for tweets related to 5G.…”

Section: Related Workmentioning

confidence: 99%

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

et al. 2021

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With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people’s perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.

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Section: Related Workmentioning

confidence: 99%

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

et al. 2021

Self Cite

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“…Object cutting technology is a condition-oriented and basic image processing technology, it can quickly cut the large range of images that may contain objects under detection to increase efficiency [19]. Image recognition technology has been continuously improved and has been successfully applied in many industries, such as digital rights management (DRM) [20], robotic arm path planning [21], studies on the determination of fruit ripeness [1,4], lighting control technology [22], and diabetic retinopathy diagnosis [23][24][25][26]. With the investment of related resources and the evolution of technology, more innovative applications will be developed in the future.…”

Section: Introductionmentioning

confidence: 99%

Designing and Manufacturing of Automatic Robotic Lawn Mower

et al. 2021

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This study is about the manufacturing of a personified automatic robotic lawn mower with image recognition. The system structure is that the platform above the crawler tracks is combined with the lawn mower, steering motor, slide rail, and webcam to achieve the purpose of personification. Crawler tracks with a strong grip and good ability to adapt to terrain are selected as a moving vehicle to simulate human feet. In addition, a lawn mower mechanism is designed to simulate the left and right swing of human mowing to promote efficiency and innovation, and then human eyes are replaced by Webcam to identify obstacles. A human-machine interface is added so that through the mobile phone remote operation, users can choose a slow mode, inching mode, and obstacle avoidance mode on the human-machine interface. When the length of both sides of the rectangular area is input to the program, the automatic robotic lawn mower will complete the instruction according to the specified path. The chip of a Digital Signal Processor (DSP) TMS320F2808 is used as the core controller, and Raspberry Pi is used as image recognition and human-machine interface design. This robot can reduce labor costs and improve the efficiency of mowing by remote control. In addition to the use as an automatic mower on farms, this study concept can also be used in the lawn maintenance of golf courses and school playgrounds.

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“…Ankle sprains were reported as the most commonly reported sports injury. Another wearable sensor worn on the skin could detect hydration levels and alert against complications that could arise in diabetic persons [ 28 ]. For serious afflictions like heart ailments, a touch device was developed to track bio-parameters like an electrocardiogram; it tracked SpO 2 , skin temperature, and the physical activity of the patient [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms

Tanwar

Chauhan

Singh

et al. 2020

Sensors

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Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.

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Non Invasive Skin Hydration Level Detection Using Machine Learning

Cited by 33 publications

References 26 publications

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

Designing and Manufacturing of Automatic Robotic Lawn Mower

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms

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