# Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping
## 1 Introduction  
In recent years, people have been seriously affected in terms of physical health and quality of life due to motor dysfunction accompanied by cervical spondylosis, stroke, Alzheimer's disease, etc. Additionally, the proportion of individuals experiencing motor function disorders due to various accidents is also on the rise [1]. Research has shown that recovering the ability to engage in activities of daily living as early as possible is the main goal of rehabilitation for patients, and specific daily movement training, such as activities of daily living training, is the main means of improving the probability of limb recovery [2]. The traditional action training program has the disadvantages of over-reliance on doctors' professional ability, boring and cumbersome processes, and low training efficiency [3]. With the development of technologies such as artificial intelligence and big data, intelligent rehabilitation medical treatment has received widespread attention [4]. Intelligent rehabilitation medical treatment records and analyzes the evaluation process, simplifying training and saving human resources through an intelligent action evaluation system, which has the advantages of high convenience, efficiency, and accuracy [5]. Therefore, the automatic assessment of daily movements in rehabilitation training can standardize assessment standards and is of great significance in providing training guidance [6]. Research on human action evaluation can be categorized into two types based on the method of data acquisition: that on wearable sensors and that on non-wearable sensors [7]. The former uses inertial sensors and potentiometers in wearable devices, which are cost-effective and free from angle occlusion issues but may cause discomfort and restrict natural action [8]. The latter employs vision sensors such as Kinect and Xtion PRO to provide natural, convenient, and non-intrusive 3D key-point data acquisition, which is essential to detecting abnormal actions and evaluating action quality [9]. Due to the cost and data advantages of non-wearable sensors, research in this area is flourishing. [PERSON] used Kinect sensors to obtain depth images, proposed the use of multiple Kinect detection of human key points to solve the action occlusion problem, and realized action quality evaluation for rehabilitation training [10]. [PERSON] compared human key-point data obtained from Kinect v2 and the Vicon optical action capture system to achieve human action evaluation, including anomaly detection and quality evaluation [11]. [PERSON] used an Azure Kinect camera (Microsoft, Redmond, WA, USA) to capture a 3D model of the human body and selected actions such as standing, stepping, leg lifting, and squatting to calculate joint angles, enabling knee function evaluation and exercise evaluation [12]. In terms of model building and algorithm research, more and more research in the literature explores action evaluation methods based on machine learning. [PERSON] quantitatively evaluated features through multiple supervised learning models and employed end-to-end recurrent neural networks to improve action classification and evaluation [13]. [PERSON] used OpenPose to extract human key points and angle features from RGB images and successfully implemented tennis action evaluation [14]. [PERSON] addressed the need for the automation of action evaluation in rehabilitation medicine and used time-domain filtering and a convolutional neural network to classify fine actions, achieving efficient action evaluation [5]. Most current studies rely on comparisons with manual predefined templates when quantifying action quality, which suffer from inconsistent time-series length. Some research has proposed time alignment algorithms such as Dynamic Time Warping (DTW) and its variants to address this problem [15]. [PERSON] proposed standard component analysis and extended it to standard time warping to solve the temporal alignment problem of human action sequences [16].