# Design, Implementation, and Field Testing
## 2 Background and Related Work  
There is a vast literature on human activity tracking using computer vision. For a comprehensive review of the research on human activity tracking using Microsoft Kinect, readers are referred to [5; 7]. Human activity recognition is typically accomplished via two approaches: (1) learning based; and (2) rule based. The learning-based approach relies on the existence of pre-collected and pre-labeled data for training or template comparison [8; 9]. The main benefit of the learning-based approach is that no expert knowledge is needed to define the activity to be recognized. The downside is the lack of precise information such approach can provide. Quite often, the detection (i.e., the classification) of an activity is insufficient. For example, on the back-ending activity, the degree of bending contains very valuable information that should be recorded and reported. The rule-based approach requires expert knowledge on the activity to be detected, and it also requires test runs to find the right error bounds on the activity detection. The rule-based approach is less computationally intensive and could capture precise information regarding the detected activity, such as the back-flexion angle. For PACTS, we choose to follow a rule-based approach that we developed previously [6; 10; 11; 12]. It fits better with the requirement for PACTS than a competing rule-based approach [13] because our rule-based framework could dynamically determine the starting and ending frame of an activity, and it can be integrated seamlessly with our alerting mechanism. Alerts/alarms have been well studied in the field of network/computer security (e.g., intrusion detection) [14; 15; 16] and medicine [17; 18; 19]. The main research objective on alerts/alarms generation is to establish the boundary for the normal condition. In the area of medicine, because the target is the individual patient's condition, the decision on when to generate an alarm is even more challenging than that for intrusion detection because the boundary conditions for different patients may vary significantly. The alert generation problem in our study roughly belongs to the area of medicine, although in our system an alert is triggered based on how a worker moves instead of based on his/her vital signs. Alert/alarm generation typically follow the following three approaches: (1) rule based; (2) model based; and (3) machine-learning based. In the rule-based approach, one or more rules are used to define normal or abnormal conditions. In the security area, this approach has been widely used. For example, one of the most common network-based intrusion tolerance tools, Snort, allows its users to define sophisticated rules for alert/alarm generation [20]. In the area of medicine, rules based on threshold values for a patient's vital signs [17; 18; 19] or correct movements [10; 11] have been used for alarm generation. A common issue with the rule-based approach is that a large number of alerts/alarms may be generated with high rate of false positives for a complex system. To ensure higher accuracy and reasonable alert/alarm frequency, initial alerts/alarms are often aggregated via correlation [15; 16]. In the model-based approach, a scientific model is constructed to predict the behavior of the target system (such as a computer system, a network, or a person) based on the understanding of the system. This approach has been used in some areas of medicine [21; 22]. The challenges for this approach include accurate understanding of the system, accurate measurement of the system output, and making the right tradeoff between complexity and efficiency [23]. The machine-learning based approach is appropriate when the target system cannot be accurately modeled, such as the behavior of a computer system/network [24], or some physiological processes of a patient [25; 26]. By collecting large amount of training data, statistical models can be trained and used to predict the expected output of the target system [25; 26; 27].