Dear Colleagues,

In the past decade, human activity recognition (HAR) has evolved into a very active research topic with applications in many fields such as security and surveillance and smart environments, including smart homes and healthcare. Biomedical signal processing (BSP), on the other hand, has a long history and tradition in the medical field as biomedical signals provide useful information for clinicians to help them make decisions for clinical diagnoses, patient monitoring, and treatment management. Recently, however, due to the ubiquity of sensors and the availability of new classes of algorithms, entirely new applications are becoming possible. Three trends are fueling this development. First of all, ubiquitous (non-invasive) sensors provided by the Internet of things (IoT), smart phones, smart watches, fitness trackers, etc., generate an ever-increasing amount of data suitable for analysis. Secondly, new classes of algorithms, in particular in the field of machine learning, such as deep learning convolution networks, RNNs, and transformers, enable a better automated understanding of the data. Third, computation power not available in previous decades is now a reality even on edge devices.

In general, there is a significant signal processing challenge in extracting reliable state parameters from the often noisy and imperfect data to characterize the physiological state or HAR. Whilst for applications in the medical field, the potential to help clinicians, medical doctors, and patients themselves in making better real-time decisions is high, the data quality requirements are challenging, too.

The aim of this Special Issue is to bring together original research and review articles researching the potential and challenges of extracting physiological parameters characteristic but not limited to HAR and using traditional and modern techniques of interpreting BSP in both the medical and non-medical fields.

Therefore, this Special Issue encourages the submission of state-of-the-art research in HAR and biomedical signal processing applications, as well as fundamental research relevant to the subject. Topics of interest include (but are not limited to) the following subject categories:

• New algorithms for HAR and/or BSP, including statistical analysis, machine learning, and deep learning;
• Data preparation and cleansing techniques for time series in HAR and/or BSP;
• Architecture of HAR and/or BSP applications in medical and non-medical fields;
• Sensor fusion in HAR and/or BSP applications in medical and non-medical fields;
• Wearable HAR and/or BSP applications in medical and non-medical fields.

Prof. Dr. Jörg Schäfer
Prof. Dr. Matthias Wagner
Prof. Dr. Gholamreza Anbarjafari
Guest Editors

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• human activity recognition (HAR)
• bio medical signal processing (BSP)
• biomedical informatics
• neural networks
• signal processing
• wearables
• signal processing of data collected using noninvasive sensors
• electrocardiogram (ECG)
• electroencephalogram (EEG)
• electromyogram (EMG)
• radio signals (RSSI)
• channel state information (CSI)
• Internet of things (IoT)