Game, Set, Match: Revolutionizing Tennis Court Availability with an … – Medium

Problem Statement

According to the latest statistics from the United States Tennis Association, there are an estimated 270,000 courts across the country to cater to the 23.6 million tennis players. As an avid tennis player myself, I have experienced the challenge of locating available courts, as information on court availability is often limited or inaccurate. This results in wasted time spent driving around to different locations or waiting for a court to become free. In light of these limitations, I began to consider the use of technology as a solution to this problem. The development of a mechanism that can accurately provide information on the status of various courts, such as whether they are occupied or vacant, as well as an estimation of when players may finish their games, would be of great value to tennis enthusiasts. By leveraging technology to address these challenges, the game of tennis can become more accessible and enjoyable for players across the country.

Several solution options were considered in order to solve this problem.
1. To manage court usage, one solution is to create a reservation system. However, this approach may have some challenges. Some players may reserve a court but need to show up or stay for a shorter time than they indicated. Additionally, some players may prefer the flexibility of playing whenever they want without having to reserve in advance.
2. A different solution is to create a check-in system at the courts and ask players to specify their checkout time. Then I would use that information to display to users when players have checked in and estimated departure time. However, most users may forget to check in and it is difficult to change or encourage user behavior. Thus players may still end up going to the courts and find it occupied.
3. A third solution is to implement a human occupancy detection mechanism that can be installed for every court to detect the real-time status of courts. There are various methods of detecting occupancy such as IoT devices consisting of various sensors, cameras, AI/ML, etc. This option would be most reliable as it solely depends on the accuracy of the device itself, not on human cooperation.

Thus, I decided to follow through with the research using this approach — human presence detection with IoT systems.

Research Question

What is the most effective and economical approach, utilizing machine learning algorithms or electrical engineering techniques, to accurately predict human presence and occupancy on a tennis court?

Methods

I. Methods for Human Presence Detection. Various methods are available for detecting human presence, including video, audio, or sensor technology. In this study, I tested several such methods, namely video with machine learning human detection (computer vision), microwave sensing with Arduino, passive infrared sensing with Arduino, and Bluetooth detection with ESP32. I chose these methods because they are cost-efficient and are known to accurately predict human presence. While other options like machine learning with the NVIDIA Jetson Nano or the X4M300 UWB sensor might provide better performance, their higher cost makes them impractical for scaling to multiple courts.

II. Method #1 — Passive Infrared Sensing with Arduino. A passive infrared (PIR) sensor is an electronic device that detects the infrared radiation from objects within its viewing area. They operate by detecting changes in heat in the environment resulting in a binary output. Typically utilized in motion detection applications such as automated lighting controls and security systems, PIR sensors can be connected to an Arduino microcontroller and tuned with delay and sensitivity parameters. This integration is facilitated by the straightforward digital input/output pins required for interfacing with Arduino, rendering PIR sensors an inexpensive, dependable, and easily configurable option for motion detection.

For method testing, I employed the HiLetgo HC-SR501 PIR Infrared Sensor. Specifications — operating voltage range: DC 4.5–20V, angle: <100° cone angle lens, maximum range: 10m. See Fig. 1 for the circuit diagram.

III. Method #2 — Microwave Sensing with Arduino. Microwave sensors, also known as radar or Doppler sensors, use radio waves to detect the presence and motion of objects. Specifically, they operate under the Doppler effect, where the antenna on the sensor emits radio waves at a certain frequency (3.2GHz for the RCWL-0516), and the strength of the signal that returns depends on the size of the object that reflects it. Such sensors are widely used in motion detection, proximity sensing, and level sensing applications. When a microwave sensor is integrated with an Arduino microcontroller, the sensor can return a binary output true or false.

III. Method #3 — BLE Sensing with ESP32. BLE (Bluetooth Low Energy) device sensing is a cutting-edge wireless technology for communicating with Bluetooth devices. This technology involves the utilization of small, low-power devices like smartphones, smartwatches, or other wearable devices equipped with a BLE chip. These devices establish communication with BLE sensors placed around a room or building, which detect their presence and transmit this information to a central system. In theory, if a Bluetooth device were to be detected at a sports court, this could suggest that a person is present at the court, as most players will carry a phone, headphones, or other devices.

III. Method #4 — Machine Learning with Camera and Raspberry Pi. Live video classification, specifically utilizing machine learning and a Raspberry Pi (computer vision), is a technique employed to analyze live footage. This technology employs a Raspberry Pi, a small and low-power computer, to process video data in real-time. The Raspberry Pi, equipped with a camera module, would run a machine learning algorithm to analyze the video and classify the objects in it. The system can be trained to recognize human characteristics, such as body shape, movement, and other features, thereby enabling it to determine the presence of people in a particular area and transmit the information accordingly. I explored YOLOv3 as the ML model, which uses a single convolutional network that predicts bounding boxes and the resulting probabilities. In comparison to the Jetson Nano, which may be more efficient and powerful, the Raspberry Pi and Pi Camera combination offers a cost-effective solution that can be easily scaled.

Results

I. Method #1 Performance. To evaluate the performance of the HiLetgo HC-SR501 PIR Infrared Sensor (operating voltage range: DC 4.5–20V, angle: <100° cone angle lens, maximum range: 10m), I coupled the sensor with an Arduino Uno microcontroller in accordance with the circuit diagram illustrated in Figure 1. The experimental design consisted of several trials with the experimenter positioned at varying angles, distances, and postures in front of the sensor. Our results indicated that the sensor yielded higher 78% accuracy in detecting significant movements such as walking, running, or shuffling, but exhibited a modest 68% accuracy rate in detecting minor actions such as waving a hand, moving slowly, or standing still, as evidenced in Table 1. False-negative outcomes were common, meaning that the sensor did not detect movement despite human presence.