Human Activity Recognition and Movement Analysis on Smartphones and Personal Devices
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".
Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 12116
Special Issue Editors
Interests: wearable sensors; machine learning; activity recognition; inertial sensors; movement analysis; gait parameters estimation; automatic early detection of gait alterations; sports bioengineering; mobile health
Special Issues, Collections and Topics in MDPI journals
Interests: high-level programming methodologies and frameworks for body sensor networks; collaborative and cloud-assisted body sensor networks; pattern recognition and knowledge discovery algorithms on physiological signals; human activity recognition; ECG analysis; emotion recognition; interoperability on the Internet-of-Things
Special Issues, Collections and Topics in MDPI journals
Interests: wearable sensor systems for human motion capture; magneto-inertial measurement units; computational methods for wearable sensor systems; multisensor fusion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowadays, the widespread availability of personal devices including smartphones and smartwatches with embedded sensing sources and high-performance computational units is driving the development of solutions for the automatic recognition and analysis of human movement. This opens scenarios for the large-scale self-assessment of natural movement for everyday life activities at every age, including sport and fitness applications. Additional applications could span from the early detection of movement disorders using personal devices to human–robot interactions, passing through fall detection/fall risk assessment in the elderly, rehabilitation tracking and treatment assessment using smartphone-embedded sensors or sensing devices connected to it and to the cloud.
Activity recognition and activity analysis methodologies, after two decades of continuous improvements, are now capable of extracting reliable information about the activity being performed at different levels of detail, but the great advantage of using personal devices lies in the capability to track fully naturalistic conditions, and ecological ways of moving.
In such a context, there are still many challenges to face in order to make activity recognition and analysis capable of reliably dealing with the huge variability of out-of-the-lab conditions. Challenges span from the definition of new computational strategies for inertial pre-processing sensors to adaptive and personalized classification tools. In addition, the challenging concept of group/multi-user activity is emerging, which requires novel collaborative, context-aware strategies to achieve high recognition results.
Potential topics include, but are not limited to, the following:
- Activity recognition methods
- Activity annotation tools for naturalistic conditions
- Personalized or adaptive solutions for activity recognition
- Computational methods for movement analysis
- Sensor fusion and machine learning methods for movement recognition and analysis
- Collaborative approaches for multi-user activity recognition
- Smart device based implementation of movement recognition and analysis methods
- Health self-assessment using mobile and wearable technologies
- Recognize/analyze gestures using smart devices (activities of daily living, sports, body language)
- Activity recognition and analysis to track rehabiltation or therapy treatment in everyday life
- Early detection of movement alterations
- New hardware connected to portable devices for quantified-self applications
- Big data and cloud-based solutions for movement recognition and analysis
- Information management for mobile device based movement recognition and analysis
Dr. Andrea Mannini
Dr. Raffaele Gravina
Prof. Angelo Maria Sabatini
Guest Editors
Manuscript Submission Information
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Keywords
- Smart and personal devices
- Activity recognition
- Movement analysis
- Machine learning
- Sensor fusion
- m-Health
