RadSense: Enabling one hand and no hands interaction for sterile manipulation of medical images using Doppler radar

Miller, Elishiah; Li, Zheng; Mentis, Helena M.; Park, Adrian; Zhu, Ting; Banerjee, Nilanjan

doi:10.1016/j.smhl.2019.100089

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…Rather than using the machine-learning technique, another study [31] used a differentiate and cross-multiply (DACM)-based signal processing technique to classify seven hand gestures. Similarly, several studies [21,58,63], used k-Nearest Neigh- Table 3 shows that, for the classifiers based on deep-learning algorithms, the 2D and 3D data representation shown in Figure 7c-f is commonly used. For example, the researcher work presented in [54] constructed an RGB image by observing the change in Doppler frequency over a specific time duration, and used this image as input to a three-layerd DCNN algorithm, resulting in 87% accuracy with 10 hand gestures.…”

Section: Hgr Through Sfcw (Doppler) Radarmentioning

confidence: 99%

“…Rather than using the machine-learning technique, another study [31] used a differentiate and cross-multiply (DACM)-based signal processing technique to classify seven hand gestures. Similarly, several studies [21,58,63], used k-Nearest Neighbor kNN for gesture recognition. In [63], the authors used Doppler frequency and time features, along with several additional features based on variations in physical attributes, while performing the gesture, such as the direction and speed of hand movement.…”

Section: Hgr Through Sfcw (Doppler) Radarmentioning

confidence: 99%

“…A total of 14 gestures were exploited in this study. Miller et al [21] recently proposed a wearable sensor based on the Doppler radar, named RadSense, intended for use in operation theaters. The sensor is pseudo-contactless and is supposed to be worn on the chest.…”

Section: Hgr Through Sfcw (Doppler) Radarmentioning

confidence: 99%

“…A work presented by Ahmed and Cho [12] (2020) demonstrated a performance comparison of different Deep Convolutional Neural Network (DCNN)-based deep-learning algorithms using multiple radars. In addition to that, hand gesture recognition through radar technology also found application in the operating room to assist medical staff in processing and manipulating medical images [21].…”

Section: Introductionmentioning

confidence: 99%

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Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review

et al. 2021

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Human–Computer Interfaces (HCI) deals with the study of interface between humans and computers. The use of radar and other RF sensors to develop HCI based on Hand Gesture Recognition (HGR) has gained increasing attention over the past decade. Today, devices have built-in radars for recognizing and categorizing hand movements. In this article, we present the first ever review related to HGR using radar sensors. We review the available techniques for multi-domain hand gestures data representation for different signal processing and deep-learning-based HGR algorithms. We classify the radars used for HGR as pulsed and continuous-wave radars, and both the hardware and the algorithmic details of each category is presented in detail. Quantitative and qualitative analysis of ongoing trends related to radar-based HCI, and available radar hardware and algorithms is also presented. At the end, developed devices and applications based on gesture-recognition through radar are discussed. Limitations, future aspects and research directions related to this field are also discussed.

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Section: Hgr Through Sfcw (Doppler) Radarmentioning

confidence: 99%

“…Rather than using the machine-learning technique, another study [31] used a differentiate and cross-multiply (DACM)-based signal processing technique to classify seven hand gestures. Similarly, several studies [21,58,63], used k-Nearest Neighbor kNN for gesture recognition. In [63], the authors used Doppler frequency and time features, along with several additional features based on variations in physical attributes, while performing the gesture, such as the direction and speed of hand movement.…”

Section: Hgr Through Sfcw (Doppler) Radarmentioning

confidence: 99%

Section: Hgr Through Sfcw (Doppler) Radarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review

et al. 2021

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“…The received signal is then mixed with the transmitted signal to down convert to an intermediate frequency signal, representing either the Doppler frequency of the human motion performed by a person's activity or a time shift based on the distance to the person performing the activity. We refer the reader to Miller et al. (2019) ; Wan et al.…”

Section: Introductionmentioning

confidence: 99%

Smart homes that detect sneeze, cough, and face touching

2021

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Smart healthcare solutions using the internet of medical things for hand gesture recognition system

Mahmoud

Fouad

Soliman

2020

Complex Intell. Syst.

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Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively.

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RadSense: Enabling one hand and no hands interaction for sterile manipulation of medical images using Doppler radar

Cited by 9 publications

References 15 publications

Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review

Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review

Smart homes that detect sneeze, cough, and face touching

Smart healthcare solutions using the internet of medical things for hand gesture recognition system

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