Few-Shot User-Definable Radar-Based Hand Gesture Recognition at the Edge

Mauro, Gianfranco; Chmurski, Mateusz; Servadei, Lorenzo; Cuéllar, Manuel Pegalajar; Morales, Diego P.

doi:10.1109/access.2022.3155124

Cited by 7 publications

(14 citation statements)

References 54 publications

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“…In the user-dependent scenario, the best recognition rate with wood is 96.1% with the configuration T =4, N =33, and AP= (1,2,3,6,8,9), resulting in an execution time of 5.05 ms, thereby making it eligible for dynamic, real-time interaction. With the PVC, the best recognition rate is 83.7% with the configuration T =4, N =38, and AP= (4,5,7,10,11,12) for an execution time of 5.72 ms. Gestures performed in front of the glass are recognized with a rate of 67% with the configuration T =4, N =38, and AP= (1,2,3,4,5,6,7,8,9,10,11,12), leading to an execution time of 8.47 ms.…”

Section: ) Results After the Background Subtractionmentioning

confidence: 99%

“…With the PVC, the best recognition rate is 83.7% with the configuration T =4, N =38, and AP= (4,5,7,10,11,12) for an execution time of 5.72 ms. Gestures performed in front of the glass are recognized with a rate of 67% with the configuration T =4, N =38, and AP= (1,2,3,4,5,6,7,8,9,10,11,12), leading to an execution time of 8.47 ms. Fig.…”

Section: ) Results After the Background Subtractionmentioning

confidence: 99%

“…In view of all this work, we can apply a DL algorithm, but we are keen to put a template-based [50] recognizer [51] to the test because it allows us to customize the gestures by modifying templates only, with a reasonable number of templates [12], and without re-training [52], [53]. Furthermore, we are siding with the mobile radars that are widely available on the market by taking the Walabot DIY 2 radar [3], [5], [15], [26], [45], [54], [55].…”

Section: A Radar-based Gesture Interaction 1) Performancementioning

confidence: 99%

“…• Number of sampling points (N ): represents the number of points per gesture template: N ={x ∈ N|4≤x≤40}. • Antenna pairs (AP): defines the set of antenna pairs from which the data were used: AP={ (1,2,3,6,8,9), (4,5,7,10,11,12), (1,2,3,4,5,6,7,8,9,10,11,12)}. These three sets demonstrated admissible results [26].…”

Section: ) Independent Variablesmentioning

confidence: 99%

“…On one hand, radar-based gesture recognition systems have demonstrated their effectiveness [10], reliability, and robustness [11] despite being influenced by environmental conditions [12]. Most studies [12]- [18] have reported their perfor-mance in terms of high recognition accuracy rates, making dynamic [16], [17], real-time [19]- [21] interaction feasible. High-frequency, wide-bandwidth radars detect even the finest movements [22], such as the fingers of a hand [23], at a reasonable distance [24].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Sluÿters,

Lambot,

Vanderdonckt

et al. 2024

IEEE Access

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Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic, which makes it appropriate for recognizing gestures in environments with poor visibility, limited accessibility, and privacy sensitivityWhile the performance of radar-based gesture recognition in these environments has been extensively researched, the preferences that users express for these gestures are less known. To analyze such gestures simultaneously according to their user preference and their system recognition performance, we conducted three gesture elicitation studies each with n 1 =30 participants to identify user-defined, radar-based gestures sensed through three distinct materials: the glass of a shop window, the wood of an office door, and polyvinyl chloride in an emergency. On this basis, we created a new dataset of nine selected gesture classes for n 2 =20 participants repeating twice the same gesture captured by radar through three materials, i.e., glass, wood, and polyvinyl chloride. To uniformly compare recognition rates in these conditions with sensing variations, a specifically tailored procedure was defined and conducted with one-shot radar calibration to train and evaluate a gesture recognizer. 'Wood' achieved the best recognition rate (96.44%), followed by 'Polyvinyl chloride' and 'Glass'. We perform a preference-performance analysis of the gestures by combining the agreement rate from the elicitation studies and the recognition rate from the evaluation.

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Section: ) Results After the Background Subtractionmentioning

confidence: 99%

Section: ) Results After the Background Subtractionmentioning

confidence: 99%

Section: A Radar-based Gesture Interaction 1) Performancementioning

confidence: 99%

Section: ) Independent Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Sluÿters,

Lambot,

Vanderdonckt

et al. 2024

IEEE Access

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Context-adaptable radar-based people counting via few-shot learning

Mauro¹,

Martínez-Rodríguez²,

Ott³

et al. 2023

Appl Intell

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In many industrial or healthcare contexts, keeping track of the number of people is essential. Radar systems, with their low overall cost and power consumption, enable privacy-friendly monitoring in many use cases. Yet, radar data are hard to interpret and incompatible with most computer vision strategies. Many current deep learning-based systems achieve high monitoring performance but are strongly context-dependent. In this work, we show how context generalization approaches can let the monitoring system fit unseen radar scenarios without adaptation steps. We collect data via a 60 GHz frequency-modulated continuous wave in three office rooms with up to three people and preprocess them in the frequency domain. Then, using meta learning, specifically the Weighting-Injection Net, we generate relationship scores between the few training datasets and query data. We further present an optimization-based approach coupled with weighting networks that can increase the training stability when only very few training examples are available. Finally, we use pool-based sampling active learning to fine-tune the model in new scenarios, labeling only the most uncertain data. Without adaptation needs, we achieve over 80% and 70% accuracy by testing the meta learning algorithms in new radar positions and a new office, respectively. Graphical abstract

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End-to-End Dynamic Gesture Recognition Using MmWave Radar

Parida

et al. 2022

IEEE Access

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Millimeter-wave (mmWave) radar sensors are a promising modality for gesture recognition as they can overcome several limitations of optic sensors typically used for gesture recognition. These limitations include cost, battery consumption, and privacy concerns. This work focuses on finger level (called micro) gesture recognition using mmWave radar. We propose a set of 6 micro-gestures that are not only intuitive and easy to perform for the user but are distinguishable based on Doppler and angle variation in time. For gesture recognition, we propose an end-to-end solution including an activity detection module (ADM) that automatically segments the data and the gesture classifier (GC) that takes the segmented data and predicts the gesture. Both the ADM and GC are based on machine learning (ML) tools. We evaluate the proposed solution using data collected from 11 users and our proposed solution achieves an end-to-end accuracy of 95%. INDEX TERMS Human-computer interface, activity detection, gesture recognition, radar, machine learningConsidering radar characteristics, we select dynamic gestures rather than static hand/finger poses in our solution. Radars have limited resolution in both the angle and range. Due to form-factor constraints, radar modules on mobile devices have only a few antennas, and as a result have limited angle resolution. While large bandwidths are available at mmWave bands, the range resolution is still several centimeters, which is too coarse for differentiating fingers' positions. Fortunately, radars have superb Doppler (speed) measurement capability. The Doppler resolution is inversely proportional to the duration of the radar coherent processing duration, which is a design parameter. The high Doppler resolution enables the radar to capture and potentially distinguish

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Few-Shot User-Definable Radar-Based Hand Gesture Recognition at the Edge

Cited by 7 publications

References 54 publications

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Context-adaptable radar-based people counting via few-shot learning

End-to-End Dynamic Gesture Recognition Using MmWave Radar

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