WHC 2022 Abstracts

Area 1 - Wearable HealthCare
Short Papers

Area 1 - Wearable HealthCare

Short Papers
Paper Nr: 1
Title:

A Practical Wearable Sensor-based Human Activity Recognition Research Pipeline
Authors:

Hui Liu, Yale Hartmann and Tanja Schultz
Abstract: Many researchers devote themselves to studying various aspects of Human Activity Recognition (HAR), such as data analysis, signal processing, feature extraction, and machine learning models. In response to the fact that few documents summarize and form intuitive paradigms for the entire HAR research pipeline, based on the purpose of sharing our years of research experience, we propose a practical, comprehensive HAR research pipeline, called HAR-Pipeline, composed of nine research aspects, aiming to reflect the overall perspective of HAR research topics to the greatest extent and indicate the sequence and relationship between the tasks. Supplemented by the outcomes of our actual series of studies as examples, we demonstrate the proposed pipeline’s rationality and feasibility.
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Paper Nr: 2
Title:

Classification and Direction Detection of Ambient Sounds on Microsoft HoloLens to Support Hearing-impaired People
Authors:

Beauclair D. Ngnintedem, Eric Mense, Johannes Rückert and Christoph M. Friedrich
Abstract: Hearing-impaired people are exposed to greater dangers in everyday life, due to the fact that they are not able to perceive danger and warning signals. This paper addresses this problem by developing an application, that could help by classifying and detecting the direction of ambient sounds using Microsoft HoloLens 2 devices. The developed application implements a client-server architecture. The server-side REST-API supports not only the classification of sounds from audio files via deep-learning methods, but also allows the results of the sound source localization to be saved and read. The sound source localization is performed by a Maix Bit microcontroller with a 6-channel microphone array. For the user integration and interaction with the application, a 3D scene has been designed using Unity and the Mixed Reality Toolkit (MRTK). The implemented application showcases how classification and direction detection of ambient sounds could be done on the Microsoft HoloLens to support hearing-impaired people.
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Paper Nr: 3
Title:

Machine-learning-driven Wearable Healthcare for Dementia: A Review of Emerging Technologies and Challenges
Authors:

Akio Sashima
Abstract: As personal mobile devices, such as smartphones and smartwatches, are increasingly commoditized, it has become easier to measure individual physiological and physical states and record them continuously. Applying machine learning techniques to the data, we can detect early signs of diseases in older people, such as dementia, and predict probabilities of future disorders. This review paper describes the machine learning technologies in realizing wearable healthcare for older people. First, we survey the literature on machine- learning-driven wearable technologies for the early detection of dementia. Second, we discuss issues of the datasets for constructing ML models. Third, we describe the need for a service framework to collect longitudinal data through continuous monitoring of the user’s health status. Finally, we discuss the socially acceptable implementation of the service framework.
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Paper Nr: 4
Title:

Algorithm for Onset/Offset Detection of EMG Signals for Real-time Control of a Low-cost Open-source Bionic-hand
Authors:

Sandra Rodrigues and Milton P. Macedo
Abstract: This work was carried out as part of a project to develop a low-cost open source bionic hand using electromyographic (EMG) signals. Probably the most important task for the success of this bionic hand is to achieve a correct determination of muscle activation intervals. In this paper it is presented an algorithm for the detection of Onset/Offset to be executed in an Arduino UNO. The aim of this algorithm is to be executed in this “ATmega328 microprocessor with a 16 MHz clock speed and 32 kBytes of memory in order to accomplish with effectiveness the real-time control of the bionic hand. The tests performed up to its application in real-time detection of muscle activation will also be described. The preliminary results presented show a 100% success rate in most gestures performed by the bionic hand but with the occurrence of a few false activations.
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