# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
###### Abstract  
Gesture recognition (GR) has many applications for human-computer interaction (HCI) in the healthcare, home, and business arenas. However, the common techniques to realize gesture recognition using video processing are computationally intensive and expensive. In this work, we propose to task existing visible light communications (VLC) systems with gesture recognition. Different finger movements are identified by training on the light transitions between fingers using the long short-term memory (LSTM) neural network. This paper describes the design and implementation of the gesture recognition technique for a practical VLC system operating over a distance of 48 cm. The platform uses a single low-cost light-emitting diode (LED) and photo-diode sensor at the receiver side. The system recognizes gestures from interruptions in the direct light transmission, and is therefore suitable for high-speed communication. Gesture recognition accuracies were conducted for five gestures, and results demonstrate that the proposed system is able to accurately identify the gestures in up to 88% of cases. visible light communications (VLC); gesture recognition (GR); human-computer interaction (HCI); human activity recognition (HAR); machine learning (ML); neural network; long short-term memory (LSTM); photo-diode (PD) +
Footnote †: journal: Medical  
these approaches are prone to clutter between the Tx and Rx reducing the resolution, and ultrasounds can also cause stress to petals and infants who can hear the low-frequency waves. Unlike visible light, some radio-frequency systems are precluded from use in hospitals, aircraft, or mines due to electromagnetic compatibility issues. One issue with video-based GR is that the foreground limb image needs to be distinguished from nearby clutter and background objects. As deep-learning algorithms became more powerful, the ability to delineate these images increased. However, deep learning often necessitates a high degree of storage and processing power, such as from a desktop computer. Although recent development kits including the Nvidia Jetson and Microsoft Kinect [1] greatly facilitated AI-based image processing, the hardware and computational costs can still be prohibitive. Another disadvantage of using video cameras for GR is due to privacy concerns and laws. Meanwhile, interest in photo-diode (PD)-based GR will increase with the emerging visible light communication (VLC) systems, which can be made with light emitting diodes (LEDs) at a fraction of the cost. Gesture recognition is a related field of human activity recognition (HAR), and recent developments are briefly described here. Two common methods for HAR are those based on video scene extraction and that of indirect sensing using wireless signals. Indirect sensing involves the analysis of the received signal strength signature from Wi-Fi signals that are blocked or reflected by human movement. Researchers demonstrated accuracies above 90% using support vector machines (SVM) machine learning (ML) [2; 3; 4]. However, it is currently very difficult to classify the subtle finger gestures using the wireless signals in a practical setting with a wall-mounted access-point, and it becomes harder with several people in the room. Physical activity recognition system using wrist-band based sensors were designed for wheelchair-bound patients with spinal cord injuries [5]. Smart healthcare systems are increasingly employing neural networks to categorize and automate functions [6]. Estimation of the number of people in a room was made through an analysis of reflection and blocking of visible light [7]. The long short-term memory (LSTM) algorithm is a type of recurrent neural network that can efficiently learn time-series sequences that are increasingly used in ML-based HAR systems, such as [8], for wearable activity recognition [9] and sign language translation [10].