A Novel Approach of Human Hunger Detection especially for physically challenged people

Lakshmi, S.; Kavipriya, P.; Jebarani, M.R. Ebenezar; Vino, T.

doi:10.1109/icais50930.2021.9395894

Cited by 3 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, some hunger detection methods have been applied for clinical and behavioral assessments [ 4 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Table 1 lists the sensors and systems used in the reviewed studies.…”

Section: Related Workmentioning

confidence: 99%

“…Lakshmi et al [ 22 ], proposed a method to detect hunger specifically in physically disabled people. The main goal was to communicate using the brain’s thoughts without muscle control, specifically for severely paralyzed people with a non-invasive approach to make the task less complex and more convenient.…”

Section: Related Workmentioning

confidence: 99%

“…In general, there are very few studies [ 4 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] on the subject that we investigate. However, each of them has some limitations; for example, the data collection method used by Krishnan et al [ 4 ] was invasive, and the results of Bellmann et al [ 19 ] were based on gastrointestinal models.…”

Section: Related Workmentioning

confidence: 99%

“…Maria and Jeyaseelan [ 23 ], and Barajas-Montiel and Reyes-Garcia [ 25 ] used microphones to record the data, which can trigger a privacy risk. The authors in [ 21 , 22 , 24 ] used hand-crafted features, while feature learning can perform as well or better than state-of-the-art [ 26 ]. To-date, no automated system for detecting hunger and satiety using multimodal physiological signals has been evaluated, nor is there a public dataset.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Irshad

Nisar

Huang

et al. 2022

Sensors

View full text Add to dashboard Cite

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP).

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Irshad

Nisar

Huang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Adapun penelitian selanjutnya terkait social distancing, penelitian menggunakan drone dengan metode yang digunakan adalah YOLO. Hasil yang didapatkan dari percobaan didapatkan hasil akurasi yang baik yaitu sekitar 90% baik untuk deteksi orang maupun deteksi kerumunan [18][19] [20][21] [22]. Fitur pertama mendeteksi pelanggaran social distancing, sedangkan fitur kedua mendeteksi pelanggaran memasuki area terlarang.…”

Section: Pendahuluanunclassified

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

Naf’an

Islami

Gushelmi

2022

mib

View full text Add to dashboard Cite

This study aims to propose a system that is able to detect crowds that have the potential to violate the Covid-19 health protocol. In this case the camera is used to capture objects in the form of images (image). Furthermore, the obtained images are processed using Deep Learning. In this case, the Convolutional Neural Network (CNN) is used. The criteria used are the classification of the number of people in the picture and the distance between each person in the picture. The distance allowed between each person is 1 meter. If there are more than 5 people in the image that have a distance between each of them less than 1 meter, then this is classified as a crowd that has the potential to violate the Covid-19 health protocol. In this study, 2 classifications were used, namely crowd and non-crowded. From the test results obtained the average value of accuracy, precision, and recall are 91.71, 91.25, and 92.65, respectively.

show abstract

Crowd Detection that Potentially Violate Covid-19 Health Protocol Using Convolutional Neural Network (CNN)

Naf’an

Islami

Gushelmi

2021

2021 International Conference on Computer Science and Engineering (IC2SE)

View full text Add to dashboard Cite

A Novel Approach of Human Hunger Detection especially for physically challenged people

Cited by 3 publications

References 11 publications

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Implementasi Deep Learning Dalam Pendeteksian Kerumunan Yang Berpotensi Melanggar Protokol Kesehatan Covid-19

Crowd Detection that Potentially Violate Covid-19 Health Protocol Using Convolutional Neural Network (CNN)

Contact Info

Product

Resources

About