Design, Implementation, and Field Testing of a Privacy-Aware Compliance Tracking System for Bedside Care in Nursing Homes

Abstract

Lower back musculoskeletal disorders are pervasive in workplaces. In the United States alone, the total cost of such injuries exceed $100 billion a year. 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. In healthcare venues, nursing homes in particular, nursing assistants are on the front line to take care of patients. 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 designed and 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 was deployed in a local nursing home for an 80-day field study in six rooms with seven participating nursing assistants. The test exposed several issues with the original design of the system. The primary issue is how to balance the privacy requirement and the usability of the system. Overemphasizing the former would negatively impact the latter. This issue is partially resolved with a leasing mechanism where the system would automatically register a nursing assistant within the lease period once she or he has manually registered with the system.

A Reversible Watermark With a New Overflow Solution

“…Step 2 (Generate Parameters): Generate several secret parameters for the later steps, including the initial value of Logistic model x 0 , and two prime numbers k 1 , k 2 . We use the following method to generate these parameters: 1) Require the user to input the password of any length s; 2) Use MD5 hash function to generate the 32-bit digest of s, denoted as a, where all the letters are lowercase; 3) Get the ASCII codes for each letter in a, to form an ASCII code vector a ; 4) Calculate r = i=2k+1 a i , k ∈ N and ∈ [1,15]; 5) Calculate x 0 = r ×10 −n , where n is the smallest natural number that let x 0 be in the range [0.1, 1); 6) Calculate the product of the 4th and the 5th number in a as m 1 . Denote the largest prime number that less than m 1 /24 as k 1 ; calculate the product of the 12nd and the 13rd number in a as m 2 .…”

“…Secondly, we adjust the overflowed pixels in the carrier image and embed the adjustment records with the proposed low distortion overflow processing algorithm (LDOPA). The contributions of this paper include: firstly, the LDOPA represents a new idea of solving the overflow problem for reversible watermarking, with a higher quality and a lower complexity; secondly, with the application of Logistic mapping, MD5 hash function, Torus mapping and Cyclic Redundancy Check (CRC) [15], the proposed scheme also enhances the security of the watermark [16].…”

Intelligent sensing and decision making in smart technologies

“…1 These hardware systems are made smart by various computational intelligence algorithms and sensing technologies. 2 This development has resulted in many emerging highly multidisciplinary research areas typically termed as smart-* technologies, including smart-healthcare, [3][4][5][6] smart-home, 7,8 smart-grid, 9 as well as smart vehicles and intelligent transportation systems. 10 These new technologies are transforming our society and have enormous economic impact.…”

A high‐accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology

“…In the past decades, the number of mobile devices (MDs) has grown at an unprecedented, 1-3 billions of devices (eg, MDs, wearable devices, sensors, and other IoT computing nodes) are connected to the internet for a wide variety of mobile applications. [4][5][6][7] One of the important applications is computation-intensive tasks, such as in-vehicle videos and virtual reality (VR). 8 To process such computation-intensive tasks, many computing capacity and energy supply are required.…”

“…[4][5][6][7] One of the important applications is computation-intensive tasks, such as in-vehicle videos and virtual reality (VR). 8 To process such computation-intensive tasks, many computing capacity and energy supply are required. [9][10][11] Unfortunately, due to the limited computation resources such as limited battery life and insufficient computing capacity, the users' quality of experience will be reduced when executing the computation-hungry applications in MDs.…”