Contactless Walking Recognition based on mmWave RADAR

Senigagliesi, Linda; Ciattaglia, Gianluca; Gambi, Ennio

doi:10.1109/iscc50000.2020.9219565

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…In this context, future research could involve the use of motion cameras, depth cameras, or wearable sensors to study movements or improve the technical capability of the player kicking the ball. Alternatively, other contactless methods could include using micro-Doppler signatures from radar sensors to distinguish between different sport functional movements [ 176 ] or using it as a screening tool for injury prevention to recognize possible abnormalities in simple walking mechanisms [ 177 , 178 ].…”

Section: Discussionmentioning

confidence: 99%

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review

Nocera

Sbrollini

Romagnoli

et al. 2023

Sensors

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American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering research in American football, highlighting the main trends and gaps. The review followed the PRISMA guidelines and gathered a total of 1629 records from PubMed (n = 368), Web of Science (n = 665), and Scopus (n = 596). The records were analyzed, tabulated, and clustered in topics. In total, 112 studies were selected and divided by topic in the biomechanics of concussion (n = 55), biomechanics of footwear (n = 6), biomechanics of sport-related movements (n = 6), the aerodynamics of football and catch (n = 3), injury prediction (n = 8), heat monitoring of physiological parameters (n = 8), and monitoring of the training load (n = 25). The safety of players has fueled most of the research that has led to innovations in helmet and footwear design, as well as improvements in the understanding and prevention of injuries and heat monitoring. The other important motivator for research is the improvement of performance, which has led to the monitoring of training loads and catches, and studies on the aerodynamics of football. The main gaps found in the literature were regarding the monitoring of internal loads and the innovation of shoulder pads.

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Section: Discussionmentioning

confidence: 99%

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review

Nocera

Sbrollini

Romagnoli

et al. 2023

Sensors

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“…In the FMCW radar system, the frequency of the transmitting wave increases linearly at a constant rate as shown in Figure 1. A short time of these signals is named a "fast time signal" or a "chirp", and multiple collected chirps are called a "frame" or a "long time signal" [20]. Hand gestures also affect the methods of recognition.…”

Section: Radar System 221 Fmcw Radarmentioning

confidence: 99%

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map

Jhaung

Lin

Zha

et al. 2022

Sensors

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There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

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“…They are ultimately inconvenient and cumbersome to be worn for long hours [1], [2]. Additionally, the medical staff must interact with the sensors and place them on the subject's body, which could be complex if patients are in frail or disabled condition [3]. The main advantage of noncontact methods is their noninvasive nature making them a solution to the problems that can be encountered with contact techniques.…”

Section: Introductionmentioning

confidence: 99%

“…This review focuses on the application of RADAR sensors, which are non-contact devices able to directly measure a target's information like the distance, the angle of arrival and velocity; this can be also classified using RADAR signals [4], [5]. Moreover, compared to RGB cameras, they pose fewer privacy concerns and are more resilient to changes in the environment, such as different lighting conditions and temperature variations.…”

Section: Introductionmentioning

confidence: 99%

“…([2023] [human detection] [UWB 7,29])[175]([2023] [heart disease detection] [UWB 4,3]);[173]([2016] [heart disease detection] [UWB]);[202]([2022] [affective computing] [FMCW 79]);[206]([2021] [affective computing] [UWB 7,29]);[134]([2022] [breath patterns classificaiton] [UWB 7,29]);[131]([2021] [breath patterns classificaiton] [UWB 7,25]);[128]([2022] [breath patterns classificaiton] [UWB 7,29]);[126]([2022] [breath patterns classificaiton] [UWB 7,29]);[124]([2020] [breath patterns classificaiton] [UWB 3,5] );[123]([2019] [breath patterns classificaiton] [UWB 3,5]);[38]([2019] [breath patterns classificaiton] [UWB 3,5]);[120]([2022] [breath patterns classificaiton] [UWB 7,29]);[148]([2023] [breath patterns classificaiton][62]);[117]([2022] [breath patterns classificaiton] [FMCW 78,5] )[112]([2022] [breath patterns classificaiton] [UWB 7,29])[200]([2023] [activities classification] [FMCW 78,5]) MLP (n=13) [183]([2023] [fatigue monitoring] [FMCW 79]); [172]([2022] [heart disease detection] [FMCW]); [151]([2021] [quality detection] [UWB 8,75]); [211]([2022] [user authentication] [FMCW 78]); [136]([2021] [breath patterns classification] [CW 24]); [184]([2021] [fatigue monitoring] [UWB 7,29]); [223]([2016] [user authentication] [CW]); [187]([2023] [human detection] [UWB 7,25]); [206]([2021] [affective computing] [UWB 7,29]); [181]([2021] [fatigue monitoring] [FMCW 78,5]); [122]([2018] [breath patterns classification] [UWB 6,8]); [15]([2022] [heart disease detection] [FMCW 79,4]); [120]([2022] [breath patterns classificaiton] [UWB 7,29]) CNN+RNN (n=4) [145]([2023] [breath patterns classificaiton] [FMCW 24]); [208]([2022] [affective computing] [FMCW 78,5]); [114]([2022] [breath patterns classificaiton] [FMCW 60 ]) RNN (n=4) [135]([2023] [breath patterns classificaiton] [UWB]); [149]([2023] [quality detection] [FMCW]); [169]([2021] [sleep s...…”

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confidence: 99%

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Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

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In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

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Contactless Walking Recognition based on mmWave RADAR

Cited by 10 publications

References 10 publications

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

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