## 1 Introduction  
State-of-the-art techniques on time-dependent biomedical signal analysis significantly exhibit their successful progress both on robust sensor architecture and elegant statistical learning-based approaches [1; 2]. Prospective experimental schemes and research in recent years has focused on the interpretations between physiological and functional expressions in terms of biomedical signals from heart rate variations, brainwave monitoring, respiration rates, and so forth [3; 4; 5; 6]. Additionally, several methods of rehabilitation based on statistical learnings have also fulfilled fruitful performances for the recovery of human activities damaged by injury or known diseases, such as heart failure [7], stroke [8; 9; 10; 11], Parkinson's disease [12] and osteoporosis [13]. Among these applications, the hardware schemes of data acquirement commonly utilized wireless sensors [14; 15; 16; 17], personal portable devices [9; 18; 16], robotic assistance [19; 20; 21; 22], or other commercial equipment [1; 23; 24]. The acquired results were then fed into relevant image- or video-based techniques [10; 11; 12; 25; 26; 17; 21; 25] for further human posture recognitions. Sensor-based approaches often offer the merits of portable convenience and contactless framework [15]. Under the hardware schemes, human activities and motional patterns were detected and recorded by employing inertial sensors [1; 2; 15; 16; 17] and/or image sensors [1; 23; 24; 27]. The acquired analog signals were then commonly analyzed using machine learning methods [15; 17; 22; 23; 27; 28; 29; 30; 31; 32]. Thus, the data acquirements of human mobility and activities can have less interventions in the duration of data collection. Informative feature extractions mainly rely on the support vector machine [27; 33], K-means clustering algorithm [33; 34], or linear discriminant analysis [35], whereas the hidden Markov model was commonly used for human activity recognition [22; 31; 33; 34]. To further increase the accuracy of posture recognition in both industry and academia, the image and inertial sensor fusion is a popular technique, performed by commercial equipment, the Microsoft Kinect [1; 10; 11; 23]. Based on the proposed experimental schemes, the approaches could be categorized as the skeleton-joint-based approach [1; 17; 24; 28; 29; 30; 31; 32] and the silhouette-based approach [23; 36]. Further combining the technique of virtual reality, these approaches can provide an alternative [10; 11; 12] for guiding correct motions in rehabilitation and can simultaneously motivate the psychological emotions of users. Although the accuracy of activity recognition can be significantly increased by adding the number of sensors set up on the skeleton points, high-budget apparatus and complex hardware structures might reduce the acceptance from users. Among relevant methods, contemporary physics-based techniques were also ambitiously developed to find effective solutions on the pattern recognition of biomedical signals. For instance, the Hilbert-Huang transform based on the method of physical energy spectrum analysis [37] can decompose analog signals into several independent modes in frequency domains. Then, the relationship between signal powers and informative features can be further delineated in specific modal spaces, so that the significant features can be extracted according to the importance of the corresponding signal powers. On the other hand, the method of multiscale entropy analysis based on classical thermal statistics [38; 39; 40] well clarifies the importance of systematic complexity in a developing biological system. The biological system, having higher systematic complexity estimated using multiscale entropy, will exhibit higher initiative to accommodate itself to the surrounding environment or external pressures. Since the aforementioned methods were established on theoretical fundamentals in both physics and information theory, abundant informative features hidden in a biological system of interest would be sufficiently extracted.