Classification of body postures using smart workwear.

BACKGROUND: Despite advancing automation, employees in many industrial and service occupations still have to perform physically intensive work that may have negative effects on the health of the musculoskeletal system. For targeted preventive measures, precise knowledge of the work postures and movements performed is necessary. METHODS: Prototype smart work clothes equipped with 15 inertial sensors were used to record reference body postures of 20 subjects. These reference postures were used to create a software-based posture classifier according to the Ovako Working Posture Analysing System (OWAS) by means of an evolutionary training algorithm. RESULTS: A total of 111,275 posture shots were recorded and used for training the classifier. The results show that smart workwear, with the help of evolutionary trained software classifiers, is in principle capable of detecting harmful postures of its wearer. The detection rate of the evolutionary trained classifier ([Formula: see text] for the postures of the back, [Formula: see text] for the arms, and [Formula: see text] for the legs) outperforms that of a TensorFlow trained classifying neural network. CONCLUSIONS: In principle, smart workwear - as prototypically shown in this paper - can be a helpful tool for assessing an individual's risk for work-related musculoskeletal disorders. Numerous potential sources of error have been identified that can affect the detection accuracy of software classifiers required for this purpose.

A Real-Time Eye Tracking System for the Detection of Eye Blinks.

INTRODUCTION: This paper proposes an eye blink detection system that automatically detects eye blinks, which can be an indicator of fatigue or cognitive load, among others. As a key feature, the real-time capability of the system is being required to use it, for example, as a monitoring system for people in potentially critical situations (e.g., drivers or operators of heavy machinery). METHODS: The system uses the Viola-Jones algorithm for face detection and the median flow tracker to track the face in video sequences. Eye detection is implemented using face proportions, and template matching is used for blink detection. RESULTS: The resulting system processes 40-47 frames per second on default consumer hardware and achieves an accuracy of 80.33% and a precision of 85.22% in the evaluation. DISCUSSION: The proposed system shows promising results under ideal viewing conditions but has difficulty maintaining high precision during head movements. The proposed system could be integrated with various health-related assistance systems to monitor the individual's well-being in real time, as long as their head is observed from the front if possible.

OWAS inter-rater reliability.

The Ovako Working posture Assessment System (OWAS) is a commonly used observational assessment method for determining the risk of work-related musculoskeletal disorders. OWAS claims to be suitable in the application for untrained persons but there is not enough evidence for this assumption. In this paper, inter-rater (inter-observer) reliability (agreement) is examined down to the level of individual postures and categories. For this purpose, the postures of 20 volunteers have been observed by 3 varying human raters in a laboratory setting and the inter-rater agreement against reference values was determined. A high agreement of over 98%(κ=0.98) was found for the postures of the arms but lower agreements were found for posture classification of the legs (66-97%,κ=0.85) and the upper body (80-96%,κ=0.85). No significant difference was found between raters with and without intense prior training in physical therapy. Consequently, the results confirm the general reliability of the OWAS method especially for raters with non-specialized background but suggests weaknesses in the reliable detection of a few particular postures.

Practical Implementation of Receiver-Oriented Encryption in STROKE OWL.

New forms of care for complex chronic diseases require substantial efforts in the collection, storage, and analysis of privacy sensible medical data. Additionally, providing practical support for those who coordinate the actual care management process within a diversified network of regional service providers is beneficial. In this paper, we present a follow-up with the current status, lessons learned, and preliminary results of the IT infrastructure in project STROKE OWL. The project aims at a comprehensive implementation of cross-sector care management for stroke patients. Patients are accompanied in their recovery by stroke pilots, who use a specially designed tablet app for the piloting. It is shown how the tablet app uses receiver-oriented encryption to support the piloting process while ensuring privacy.

Privacy by Design for Integrated Case and Care Management: Receiver-Oriented Encryption in STROKE OWL.

Research into new forms of care for complex chronic diseases requires substantial efforts in the collection, storage, and analysis of medical data. Additionally, providing practical support for those who coordinate the actual care management process within a diversified network of regional service providers is also necessary. For instance, for stroke units, rehabilitation partners, ambulatory actors, as well as health insurance funds. In this paper, we propose the concept of comprehensive and practical receiver-oriented encryption (ROE) as a guiding principle for such data-intensive, research-oriented case management systems, and illustrate our concept with the example of the IT infrastructure of the project STROKE OWL.

XML Skeleton Definitions for Human Posture Assessments.

In this paper, we show how the XML dialect SKAML (Skeletal Assessment Markup Language) can be used to use data from one or more Motion Capture systems to perform human posture assessments with multiple assessment methods. We show an implementation example using an inertial measuring suit and both OWAS and REBA assessment methods. SKAML makes it possible to implement classifiers for a Motion Capture system once and adapt the classifier by-configuration to various ergonomics assessment methods. We anticipate our work as help for researchers and developers that implement new assessment methods or motion capture systems.

Enhancing Safety of Artificially Ventilated Patients Using Ambient Process Analysis.

In this paper, we present an approach for enhancing the safety of artificially ventilated patients using ambient process analysis. We propose to use an analysis system consisting of low-cost ambient sensors such as power sensor, RGB-D sensor, passage detector, and matrix infrared temperature sensor to reduce risks for artificially ventilated patients in both home and clinical environments. We describe the system concept and our implementation and show how the system can contribute to patient safety.