## 2 Mini Survey
### Recent Trends and Developments of ADL Studies  
Various methods for estimating dominant emotions [173] have been reported from various motions, actions, and behavior patterns. [PERSON] et al. [174] widely surveyed video-based human behavior recognition studies related to ADL [175]. The results have been useful in supporting ambient assisted living systems for elderly people living independently. Generally, ADL patterns have included combing hair, applying makeup, brushing teeth, dental flossing, washing hands or face, drying hands or face, doing laundry, washing dishes, moving dishes, making tea, making coffee, drinking water or from a bottle, drinking water or from a tap, making a cold food snack, vacumming a floor, watching TV, using a computer, and using a cellular telephone [176]. To support ADL, they analyzed technical difficulties, such as occlusions and limited FoV, of conventional camera systems used in public environments. Moreover, they described that object recognition remained as a task for future study because intra-class variation occurred in unconstrained ADL scenarios. Therefore, they concluded that current system performance is far from adequate. To enhance privacy and data capacity, accelerometers and gyro sensors can be used instead of cameras. [PERSON] et al. [177] developed an ML-based framework that is useful for fall detection and ADL recognition. For their study, ADL was set as comprising six categories: standing, sitting, walking, walking upstairs, walking downstairs, and lying down. They compared four popular CML algorithms: a multilayer perceptron (MLP), \(k\)NN, quadratic SVM, and ensemble bagged trees (EBT). The experimentally obtained results using a dataset produced for use with a smartphone revealed that the accuracy of fall detection reached 100% for both quadratic SVM and EBT algorithms. Based on behavioral biometrics, [PERSON] et al. [136] proposed a recognition method for 18 physical activities including walking, jogging, using stairs, sitting, standing, kicking a soccer ball, dribbling a basketball, catching a tennis ball, typing, writing, clapping, brushing teeth, folding clothes, eating pasta, eating soup, eating a sandwich, eating chips, and drinking from a cup. The experimentally obtained results obtained with their original benchmark dataset using accelerometers and gyroscope sensors on a smartphone and a smartwatch revealed that zero-effort continuous biometrics based on normal activities and certain easy-to-perform activities of daily living are feasible and viable for gait-based biometrics. Nevertheless, their method involved wide-body motions, especially for hands and legs. Therefore, as a shortcoming, accuracy was markedly low for small and restricted motions done in a sitting posture such as eating, reading, and studying. One important application of human motion recognition technology is for health-smart homes, which have various heterogeneous sensors. [PERSON] et al. [178] proposed a DL-based method for detecting early changes in human behaviors. The method specifically examines starting, duration, disappearing, swapping, locating, and heart rate changes. For actualizing long-term continuous monitoring, they used multiple sensors such as stereo cameras, monocular cameras, time of flight (TOF) cameras, microphones, pyroelectric infrared (PIR), sonar, light detection and ranging (LiDAR), ultra-wideband LiDAR, strain gauges, barometers, and vibration sensors. The experimentally obtained results demonstrated superiority in terms of accuracy and calculation time for detection and prediction. Unsupervised DL-based deep clustering (DC) methods were found to be superior to traditional supervised and semi-supervised methods using SVM including one-class SVM, CNN, stacked auto-encoders (SAE), and \(k\)-means clustering. Nevertheless, the burdens related to annotation for long-term continuous monitoring datasets remain difficult for practical application and dissemination.