No Face-Touch: Exploiting Wearable Devices and Machine Learning for Gesture Detection

Marnilo, S.; Baldi, Tommaso Lisini; Paolocci, Gianluca; D'Aurizio, Nicole; Prattichizzo, Domenico

doi:10.1109/icra48506.2021.9561178

Cited by 7 publications

(11 citation statements)

References 15 publications

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“…The obtained results show that the proposed BLE network allows for the efficient classification of FT and nFT gestures. It should be noted that

accuracy is a comparable level to the algorithms discussed in [ 16 , 17 , 18 , 19 ]. This accuracy is quite sufficient for this type of application (taking into account the classification accuracies of 0.926, 0.90, and 0.97 in [ 18 , 22 , 24 ], respectively).…”

Section: Methodsmentioning

confidence: 81%

“…The system described in [ 16 ] utilizes an inertial measurement unit (IMU) equipped with an accelerometer and gyroscope to obtain features that characterize hand movement, such as face touching. Time-series data obtained from IMU is classified using a 1D-Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) [ 17 ], or decision tree, k-nearest neighbor, and support vector machine [ 18 ]. Similarly, in [ 19 , 20 , 21 ], accelerometer data collected from the wrist position (a smartwatch application) has been used to generate machine-learning models to recognize facial touches.…”

Section: Introductionmentioning

confidence: 99%

“…It relies on the WBAN, which comprises the three modules (nRF52840 [ 25 ]) placed on the wrists (in the form of bracelets) and one placed on the neck (as a necklace). Contrarily to the systems proposed in [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] m,face-touching gestures are recognized by analyzing the received signal strength indicator (RSSI) between the neck and wrist modules, which are connected using the Bluetooth Low-Energy (BLE) network [ 26 ]. The classification process based on RSSI data is performed using the Least Square Support Vector Machine (LSSVM) algorithm [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases

Pawlicki

Fotyga

Rewieński

et al. 2023

Sensors

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This paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements between the bracelet and necklace modules. The measured signal is cleared of noise using the exponential moving average (EMA). Next, we use a classification algorithm based on a Least-Squares Support Vector Machine (LSSVM) in order to detect facial touches. When the results of the classification indicate that the hand is moving toward the face, an alarm is sent through the neck module and the vibrator embedded in the wrist module is switched on. Based on the performed tests, it can be concluded that the proposed solution is characterized by high accuracy and reliability. It should be useful, especially for individuals who are regularly exposed to the risk of respiratory infections.

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“…The obtained results show that the proposed BLE network allows for the efficient classification of FT and nFT gestures. It should be noted that

Section: Methodsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases

Pawlicki

Fotyga

Rewieński

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…[ 13 , 14 ], physiological data (heart rate, body temperature, etc.) [ 15 , 16 , 17 ], data [ 18 , 19 , 20 ], gesture detection [ 21 , 22 ] and emotion recognition [ 23 ] of the user 24/7 and collect and store and transmit these data, while helping the users to perform many other useful micro-tasks, such as checking incoming text messages and displaying urgent information [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of Wearable Technologies in the Assessment of Physical Activity in Preschool- and School-Age Youth: Systematic Review

Sousa

Ferrinho

Travassos

2023

IJERPH

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In recent years, physical activity assessment has increasingly relied on wearable monitors to provide measures for surveillance, intervention, and epidemiological research. This present systematic review aimed to examine the current research about the utilization of wearable technology in the evaluation in physical activities of preschool- and school-age children. A database search (Web of Science, PubMed and Scopus) for original research articles was performed. A total of twenty-one articles met the inclusion criteria, and the Cochrane risk of bias tool was used. Wearable technology can actually be a very important instrument/tool to detect the movements and monitor the physical activity of children and adolescents. The results revealed that there are a few studies on the influence of these technologies on physical activity in schools, and most of them are descriptive. In line with previous research, the wearable devices can be used as a motivational tool to improve PA behaviors and in the evaluation of PA interventions. However, the different reliability levels of the different devices used in the studies can compromise the analysis and understanding of the results.

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“…Previous research studies proposed methods for face touch detection using an accelerometer, magnetometer, and acoustic-based system and studied the validity of these methods [27][28][29][30][31][32][33]. Marullo et al [27] proposed recurrent neural network (RNN) based methods to detect face touch and provide real-time feedback using accelerometer readouts collected from a smartwatch. The true positive rate and false positive rate were 100% and 3.1%, respectively, for the best method.…”

Section: Introductionmentioning

confidence: 99%

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

Fathian

Phan

et al. 2023

PLoS ONE

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One of the main factors in controlling infectious diseases such as COVID-19 is to prevent touching preoral and prenasal regions. Face touching is a habitual behaviour that occurs frequently. Studies showed that people touch their faces 23 times per hour on average. A contaminated hand could transmit the infection to the body by a facial touch. Since controlling this spontaneous habit is not easy, this study aimed to develop and validate a technology to detect and monitor face touch using dynamic time warping (DTW) and KNN (k-nearest neighbours) based on a wrist-mounted inertial measurement unit (IMU) in a controlled environment and natural environment trials. For this purpose, eleven volunteers were recruited and their hand motions were recorded in controlled and natural environment trials using a wrist-mounted IMU. Then the sensitivity, precision, and accuracy of our developed technology in detecting the face touch were evaluated. It was observed that the sensitivity, precision, and accuracy of the DTW-KNN classifier were 91%, 97%, and 85% in controlled environment trials and 79%, 92%, and 79% in natural environment trials (daily life). In conclusion, a wrist-mounted IMU, widely available in smartwatches, could detect the face touch with high sensitivity, precision, and accuracy and can be used as an ambulatory system to detect and monitor face touching as a high-risk habit in daily life.

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No Face-Touch: Exploiting Wearable Devices and Machine Learning for Gesture Detection

Cited by 7 publications

References 15 publications

Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases

Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases

The Use of Wearable Technologies in the Assessment of Physical Activity in Preschool- and School-Age Youth: Systematic Review

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

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