# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 1 Introduction  
Owing to the recent decrease in birth rates and the increase in single and divorced households, the number of elderly people living alone is increasing. Elderly people living alone spend a lot of time by themselves, which can be very dangerous as they may be unable to receive immediate help if an accident occurs. Falls are serious accidents, especially among the elderly, and can lead to severe injury or even death. Therefore, systems that can quickly detect and respond to falls among elderly people in home environments are becoming increasingly important. In recent years, active research has been conducted on fall detection systems using various sensors such as wearable sensors, camera sensors and radar sensors, and machine-learning algorithms. However, wearable sensors require regular battery charging, and are limited in detecting falls in situations where the sensor must be temporarily removed such as in wet environments, like a bathroom or swimming pool [1, 2, 3]. Camera sensors present a challenge in terms of detectability in dark environments, and significant concernsabout privacy invasion arise [4; 5; 6]. Radar sensors are gaining increasing attention in fall-detection research because they are less affected by external environmental conditions. Radar sensors primarily used for fall detection include continuous-wave (CW) radar [7; 8; 9; 10; 11; 12; 13], frequency-modulated continuous-wave (FMCW) radar [14; 15; 16; 17], and ultrawideband (UWB) radar [18; 19; 20; 21; 22]. Data acquired from radar sensors are used in machine-learning and deep-learning algorithms to detect falls. In [7], six features--such as mean and variance--were extracted from the acquired data and input into a cubic support vector machine (SVM), achieving 100% accuracy in detecting falls. In [8], the frequency distribution trajectory (FDT) generated from movements was input into a hidden Markov model (HMM) to classify three fall and four non-fall actions with 95% accuracy. In [9], machine-learning algorithms such as SVM and random forest (RF) were connected in three stages, achieving 93.24% accuracy in detecting falls. In [10], a Kalman filter was used to extract features, and k-nearest neighbors (KNN) was employed to detect falls with 92.85% accuracy. Ref. [14] proposes the dynamic range-Doppler trajectory (DRDT) method. In this method, points with a high radar cross section (RCS) are extracted from the range-Doppler map of multiple frames. These points are then represented in a three-dimensional range-Doppler frame. Four types of features were extracted from the generated DRDT map, and KNN was used to detect falls. In [15], 29 features were extracted from the point cloud data acquired using two FMCW radars, and an RF classifier was used to detect falls with 92.2% accuracy. However, when using machine-learning algorithms instead of deep-learning algorithms, features must be manually extracted, and the accuracy varies depending on the features used. In [11], a method was proposed to detect falls by inputting both thresholds and spectrograms generated from raw data, into a multistage system. This system consists of thresholding, a preliminary screening network (PSN), and a constraint reconstruction-based fall detector (CRFD). In [12], a short-time Fourier transform (STFT) was applied to the received signal generating a time-frequency spectrogram, that was converted to a grayscale image and input into a CNN to detect falls. In [16], a line kernel convolutional neural network (LKCNN) was proposed to process the received data directly without preprocessing. Additionally, a data sample generation method that utilizes multiple receiving channels and small pulse repetition times (PRT) was proposed to generate numerous samples. In [17], range-angle reflection heat maps were obtained using two fast Fourier transforms (FFTs) and then merged into one. After removing the spatial redundancy using the radar low dimension embedding (RLDE) algorithm, the data were input into an RNN for detection.