Sensor Applications in Medical Monitoring and Assistive Devices
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (15 July 2018) | Viewed by 91162
Special Issue Editors
Interests: assitive robotics; biomedical devices; sensor networks; human motion capture; machine learning; wireless communications
Special Issues, Collections and Topics in MDPI journals
Interests: cerebellar and vestibular balance disorers; ataxia metrics
Interests: Measurement of Movement; Parkinsons Disease
Special Issue Information
Dear Colleagues,
The combination of rapidly-progressing sensor technology and the wide array of medical specialty areas, provide a myriad of opportunities for biomedical applications. An example of such opportunities is found in the ability to accurately and non-invasively capture and analyze human movement. The kinematics of human motion has direct application in the diagnosis of neurodegenerative disorders. Additionally, there are a dearth of robust biomarkers in the broader area of balance disorders, which can be implemented in the measurement of disease progression or in the quantification of clinical improvement. This latter point comprises a raft of potential applications, which include the ability to gauge the success of rehabilitation programs and in the growing number of clinical treatment trials. Many neurological diseases severely compromise a patient’s ability to independently carry out their activities of daily living. Traditional subjective assessments have been found wanting, in part, because of their lack of fidelity in representing an individual’s true functional limitations.
Ambulatory devices can be used to measure vital human physiological parameters. Traditional examples of such technology include the electrocardiogram, thermometer, and electroencephalogram. These devices are commercially available in ambulatory forms, providing a means of home-based clinical monitoring. The crucial features of these devices include correct positioning on the human body, sensor quality, a relatively low power demand and wireless communication functionality (thus ensuring patient comfort). Physiological monitoring can be utilized in the diagnosis and treatment of a large number of individuals with neurological, cardiac and pulmonary diseases, such as seizures, ataxia, cardiac arrhythmias, hypertension and asthma.
Micro Electronic Mechanical Systems (MEMS) have been utilized for the development of miniaturized sensors for health monitoring systems and can be manufactured at a relatively low cost. Audio capture technology (for speech disorders,) inertial measurement units (IMU) and optico-kinetic sensors (as a means of capturing human movement parameters) are generally considered to be robust and affordable means by which human sensor systems can be developed. Indeed, the wearability and long-term use of sensors systems are vital to their successful uptake in the medical device arena.
Internet of Things (IoT) enabled devices provide tele-rehabilitation and remote therapy opportunities which minimizes patient travel (particularly from regional to urban areas) and the ability to significantly reduce the ever increasing cost of healthcare provision. The security of bio-medical data must of course be maintained throughout any process of health data acquisition, and in processes, which include data mining and machine learning, this must be robustly integrated into the development of any health monitoring or assistive devices. This is vital in providing a durable and seamless end-to-end service. The overarching objective is to enhance the uptake and promote the use of affordable, integrated functional measurement systems in a range of medical applications, including long-term physiological monitoring and the enhancement of patient’s abilities to engage in vital activities of daily living.
The development of sensor systems for monitoring and assistive devices will encompass the following areas:
- Sensor platforms and biomedical signal and data processing
- Power and wearability aspects in device design
- Pattern recognition, data mining and machine learning applied to biomedical system design
- Feature extraction and quantification of biomedical movements of speech
- Rehabilitation based devices and innovative technology
- Computational Modeling and Data Integration
- Biomedical Text Mining
- Medical Robotics
- Health informatics and Clinical Data analytics
- Biomedical Computing
- Clinical Data Analysis
- Wireless physiological monitoring devices and software application development
- Autonomic context sensing and multi-sensor fusion technology
- Remote sensing systems for patient assessment and treatment in medical and allied health clinics
- Real-time monitoring of physical daily life activities, caloric intake, sleep quality, gait, posture and other factors related to personal well-being.
- Body-sensor networks in biomedical application
- Healthcare applications based on IoT technologies.
- Cloud computing
- Clinical assisted living applications
- Human gait dynamics and balance metrics
Prof. Pubudu N. Pathirana
Dr. David J Szmulewicz
Prof. Malcolm Horne
Guest Editors
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