# Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
## 1 Introduction  
The increasing number of single-person and elderly households has drawn attention to the necessity for continuous monitoring to enable the prevention of and fast response to accidents at home. In the event of an accident, individuals often struggle to receive immediate assistance from those nearby; therefore, human activity recognition (HAR) systems should be deployed to accurately infer human activity in case of emergency. In addition, the power consumption for detecting human activity must be low at all times, and the cost of operating the HAR system must be low in order for it to be available for as many people as possible. Therefore, it is essential to research low-power and lightweight HAR systems that achieve high accuracy in human activity inference. In response to this need, HAR systems using wearable sensors with built-in accelerometers and gyroscopes for indoor accident detection [1, 2, 3], and sensors that observe the surrounding environment, such as cameras [4, 5, 6], light detection and ranging (LiDAR) [7], ultrasonic sensors [8, 9], and radar [10, 11, 12, 13, 14, 15], have been actively researched. A limitation of wearable sensors is that the device must be worn continuously to infer human activity [16]. Ultrasonic sensors have a narrow sensing range, and their performance varies significantly depending on the material or shape of the object beingdetected [17]. Camera-based sensors can cause portrait-rights infringement when collecting image data [17]. LiDAR has the disadvantages of high cost, narrow detection range, and sensors that are highly affected by the surrounding environment [17]. In comparison, radar has the advantages of having a lower cost than LiDAR and a wider range of detection and is less affected by the surrounding environment [16; 17]. Several types of data can be collected using radar; however, two main types of data can be used when performing HAR based on radar sensors--range-Doppler map and point clouds. Point-cloud data have a sufficiently high resolution to distinguish the shape of objects, and a higher inference accuracy can be achieved while maintaining a level of network complexity similar to that when using range-Doppler maps as input data [18]. [PERSON] et al. [11] constructed an HAR system using point-cloud data collected by millimeter-wave radar. They collected an MMAtivity dataset consisting of five classes and used a model consisting of a time-distributed, convolutional neural network (CNN) and a bi-directional long short-term memory (LSTM). The classification network of this HAR system achieved 90.47% accuracy using 291 K parameters. [PERSON] et al. [12] conducted human motion classification using a point-cloud dataset consisting of seven classes, which they collected themselves. The authors used a model consisting of 2D-DCNN and DRNN, and they achieved 96% accuracy with 1510 K parameters using 2D-DCNN alone, and 98% accuracy using a combination of 2D-DCNN and DRNN. [PERSON] et al. [13] constructed an HAR system using their own point-cloud dataset and a range-Doppler dataset consisting of six classes. The point-cloud data pass through a 3D CNN and LSTM, and the range-Doppler data pass through a 3D CNN. The two types of data are concatenated to classify human activity. An accuracy of 97% was achieved using a fusion network that placed two networks in parallel. [PERSON] et al. [14] used six classes of point-cloud data collected by themselves to classify human activity. The method using time-Doppler (TD) achieved 95% accuracy in combination with 3D-PointNet using 1610 K parameters, and the method using range-Doppler (RD) achieved 98% accuracy in combination with 4D-PointNet. [PERSON] et al. [15] augmented five classes of self-collected point-cloud data with segment-wise point-cloud augmentation (SPCA) to organize a dataset and infer human activity. In this study, Lite-PointNet and a bidirectional lightweight LSTM (BiLiLSTM) were used to achieve 95% accuracy.