Novel Approaches to Preventive and Occupational Telemedicine Based on Sensor Fusion
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 3463
Special Issue Editor
Interests: medical imaging; computer vision; biomedical signal processing; GPU computing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Preventive and occupational medicine requires the acquisition of physiological, psychological, physical, and prior medical data to generate a patient-specific model that can be used to detect anomalies. This Special Issue is looking for papers that deal with a decentralized multi-sensor-fusion approach based on flexible mobile data-collection platforms that can be used to create a preventive health-management system. We are looking for novel algorithms and hardware systems that can connect, acquire, and synchronize various sensors attached to a person's body and then securely transmit the fused data to a cloud server. The data used by such systems may include different mobile data sources from a remote data collection system, including directly coupled wireless sensor devices, indirectly connected devices from vendor-specific cloud solutions, and prior medical knowledge. Once received at the could server, the fused data time series is then analyzed using multivariate machine learning algorithms to detect abnormal conditions that can then be transformed into a human-understandable form that users or clinicians can quickly understand. Pertinent topics include the following:
- Temporal synchronization techniques of various sensors using local body networks
- Low-level sensor fusion algorithms to compensate measurement artifacts created by patient's activities such as motion wonders in ECG measurements and temporal coincidence algorithms to reduce false alarms in fall detection
- Multivariate temporal algorithms to detect and classify abnormal conditions
- Novel techniques to train patient-specific models from sensor data
- Novel algorithms to transform the classification of sensor data into human-understandable explanations
- Novel techniques for secure transmission and processing of sensor data, such as:
- Use of homomorphic encryption techniques to process data
- Scalable private networks capable of dealing with a large population
Prof. Dr. Pierre Boulanger
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- Sensor Fusion
- Multivariate Algorithms
- Data Body Networks
- Data Synchronization
- Homomorphic Encryption
- Cloud Processing
- Human-understandable Data Analysis
- Data Collection Platform
- Machine Learning
