Finger gesture recognition using a smartwatch with integrated motion sensors | Semantic Scholar (2024)

This paper proposes a solution to this problem by designing a low-cost data glove that utilizes multiple inertial sensors with the purpose of achieving efficient and accurate sign language recognition and compared with existing state-of-the-art algorithms using nine public datasets.

The goal is to first acquaint the reader with the important steps required to classify the movement of the human body by wearable sensors and then by using tables to determine the most used algorithms and methods for each step.

A series of bibliometric analyses on the related literature, including papers’ production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future found the subject evolution path includes sensor research, sensitivity research and multi-functional device research.

A novel data glove with two arm rings and a specially integrated three-dimensional flex sensor to capture fine grain motion from full arm and all knuckles is designed and an improved deep feature fusion network is proposed to detect long distance dependency in complex hand gestures.

This work presents a comprehensive Systematic Review of Literature (SRL) on interaction with smartwatches using gesture recognition, showing what has already been developed and what is state of the art.

A new human hand gesture dataset which could be suitable for controlling home appliances and a simple yet effective late fusion model from multimodal data for enhancing the recognition rate.

It is demonstrated that when utilizing the KNN-SFFS recommended features as the machine-learning input, the proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time, and prevents overfitted model, but also provides approximately similar machine- learning prediction accuracy compared to when all elements of feature vectors were used.

A system that exploits the Internet of Things, massive data computation, and human-robot collaboration to reach these goals and meets the demands in terms of real-time, success rate, flexibility and scalability in Industry 4.0.

The practical use of anti-motion interference wearable devices not only shows that the wearable PPG sensor in this paper can stably obtain high-quality PPG signals, but also reflects its many applications in the field of real-time blood.

WatchOut, a suite of interaction techniques that includes three families of tap and swipe gestures which extend input modalities to the watch's case, bezel, and band, and discusses the strengths, limitations, and future potential of this work.

An accelerometer-based pen-type sensing device and a user-independent hand gesture recognition algorithm that achieves almost perfect user-dependent and user- independent recognition accuracies for both basic and complex gestures is presented.

Serendipity is the first to explore the feasibility of using solely motion sensors on everyday wearable devices to detect fine-grained gestures, and has the potential to be applied to cross-device interactions, or as a tool for research in fields involving finger and hand motion.

It is shown how motion data collected with a smartwatch can be used to infer text written on a whiteboard and that the built in microphone picks up the sounds caused by the pen which can help to segment the input into individual letters.

SkinWatch provides gesture input by sensing deformation of the skin under a wearable wrist device, also known as a smart watch, which is small, thin, and stable, to accept accurate input via a user's skin.

It is shown that motion energy measured at the smartwatch is sufficient to uniquely identify user's hand and finger gestures and will enable many novel applications like remote control and finger-writing-based input to devices using smartwatch.

Experimental results have successfully validated the feasibility and effectiveness of the gesture decomposition and similarity matching-based gesture recognition algorithm and the proposed algorithm based on similarity matching improves the complex gesture recognition rate.

Using an array of force sensitive resistors worn around the wrist, the interface can distinguish subtle finger pinch gestures with high accuracy (>80 %) and speed and it is demonstrated that the number of gestures can be extended with orientation data from an accelerometer.

Digits is a wrist-worn sensor that recovers the full 3D pose of the user's hand, which enables a variety of freehand interactions on the move and is specifically designed to be low-power and easily reproducible using only off-the-shelf hardware.

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