# Design, Implementation, and Field Testing
## 1 Introduction  
Lower back musculoskeletal disorders are pervasive in workplaces. In the United States alone, the total cost of such injuries exceed $100 billion a year [1]. The lower-back injury rate in the healthcare sector is one of the highest among all industry sectors. A main risk factor for lower-back injuries is the use of improper body mechanics when doing lifting and pulling activities [2; 3]. In healthcare venues, nursing homes in particular, nursing assistants are on the front line to take care of patients. Their lower-back injury rate is more than seven times as high as the average for all workers (249 per 10,000 in healthcare compared to 34 per 10,000 [4]). Even in places where ceiling-mounted lifting equipment is installed, they are still required to handle the patient for bedside care, such as sliding the sling underneath the patient, scooping up the patient, putting on compression socks, etc. To help nursing assistants get into the habit of using proper body mechanics, we designedand implemented a privacy-aware compliance tracking system (PACTS). PACTS can track a nursing assistant for possible violation of proper body mechanics while doing bedside care and provide realtime feedback via a smart wearable device such as a smart watch worn by the nursing assistant. The system can be deployed into multiple rooms in a nursing home, and can follow a particular nursing assistant when she or he goes to different rooms to take care patients. The system was deployed in a local nursing home for an 80-day field study. Human motion tracking in PACTS is via the computer vision technology using inexpensive commercial-off-the-shelf Microsoft Kinect sensor. Enabled by the Microsoft Kinect Software Development Kit (SDK), PACTS can track up to two users with one Kinect for Xbox/Windows, and up to four users with one Kinect for Xbox One with full skeleton joint positions [5]. The accuracy of the Kinect sensor for human motion tracking has been well-established [5; 6]. However, this does not mean that it is straightforward to design a Kinect-based system for our purpose. There are two obvious challenges. First, while it is straightforward to use Kinect and its SDK to track human motion, additional mechanisms are required for Kinect-based systems to be used in healthcare venues because of the privacy of the patients must be respected and Kinect-based tracking is promiscuous, i.e., everyone who is in the view of the Kinect sensor might be tracked. Second, a major challenge of using the computer vision technology in nursing homes is to ensure reliable human motion tracking despite possibly severe occlusions caused by furniture such as bed, couch, walker, and wheelchair. Fortunately, in our target venue, only the nursing assistant's lower extremity is often occluded and, hence, the occlusion does not preclude us from detecting back-bending activities because we can use the hip center and the should center joints to estimate the trunk orientation with respect to the floor. On detection of a non-compliant activity that may increase the risk of lower-back pain, we must also design an effective intervention mechanism that alerts the nursing assistants about the event, but does not interfere with their job activities. During the field testing, we encountered another challenge not expected at the beginning, that is, how to balance the need for privacy protection and the usability of the system. Making the privacy protection stronger will inevitably reduce the usability of the system. PACTS incorporates several innovative mechanisms to cope with these challenges:  
* A privacy-aware mechanism that ensures only the consented users will be tracked even when she or he works in several different rooms during her/his shift, which protects the privacy of patients and other unrelated persons who might come to the view of the Kinect sensor. * An occlusion-resilient human activity tracking mechanism that works well in crowded rooms in nursing homes.