Hidden Markov model-based gesture recognition with FMCW radar

Malysa, Greg; Wang, Dan; Netsch, Lorin; Ali, Muhammad Yasir

doi:10.1109/globalsip.2016.7905995

Cited by 33 publications

(15 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Suh et al [70] de- Other research mainly focused on designing HGR algorithms. For example, in reference [72], the researchers used HMM to classify hand gestures with FMCW and reported 82% accuracy for five different hand gestures. Researchers in [71] used the Soli radar developed by Google [17] along with deep-learning to classify hand gestures.…”

Section: Hgr Algorithms For Fmcwmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Hgr Algorithms For Fmcwmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, similarly as human activity characterization, relevant physical features can be easily extracted from spectrograms. A commonly employed ML tool for classifying vectors of handcrafted features in HGR systems is represented by hidden Markov modelling [107]. This approach leads to classifying a new sequence of data, called observation, on the basis of a stochastic model, called hidden Markov model (HMM), which has been extracted from past observations and describes their generation.…”

Section: B Human Gesture Recognitionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

View full text Add to dashboard Cite

<p>Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.</p>

show abstract

“…• Special Device based: There have been several works in gesture and activity recognition using specialized devices and radars. [37,68,71] use FMCW [6,45] radio to monitor user activity. [68] trains a CNN classfier to detect human motion.…”

Section: Related Work 111 Device-free Sensingmentioning

confidence: 99%

“…[68] trains a CNN classfier to detect human motion. [37] trains a hidden markov model using time-velocity feature for activity recognition. [44] uses a 5-antenna receiver and a single-antenna transmitter to perform gesture classification, in the presence of three other users performing random gestures.…”

Section: Related Work 111 Device-free Sensingmentioning

confidence: 99%

Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition

Islam¹,

Nirjon²

2019

Preprint

View full text Add to dashboard Cite

The lack of adequate training data is one of the major hurdles in WiFi-based activity recognition systems. In this paper, we propose Wi-Fringe, which is a WiFi CSI-based device-free human gesture recognition system that recognizes named gestures, i.e., activities and gestures that have a semantically meaningful name in English language, as opposed to arbitrary free-form gestures. Given a list of activities (only their names in English text), along with zero or more training examples (WiFi CSI values) per activity, Wi-Fringeis able to detect all activities at runtime. In other words, a subset of activities that Wi-Fringedetects do not require any training examples at all. This is achieved by leveraging prior knowledge of these activities from another domain, i.e., text. We show for the first time that by utilizing the state-of-the-art semantic representation of English words, which is learned from a massive dataset such as the Wikipedia (e.g., Google's word-to-vector [38]) and verb attributes learned from how a word is defined (e.g, American Heritage Dictionary), we can enhance the capability of WiFi-based named gesture recognition systems that lack adequate training examples per class. We propose a novel cross-domain knowledge transfer algorithm between radio frequency (RF) and text to lessen the burden on developers and end-users from the tedious task of data collection for all possible activities. To evaluate Wi-Fringe, we collect data from four volunteers for 20 activities in two environments. We show that Wi-Fringeachieves an accuracy of up to 90% for two unseen activities and 61% for up to six unseen activities.

show abstract

Hidden Markov model-based gesture recognition with FMCW radar

Cited by 33 publications

References 7 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

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition

Contact Info

Product

Resources

About