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Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes. However, the analysis of the effectiveness and the efficiency of HAR methodologies suffers from the lack of a standard workflow, which might represent the baseline for the estimation of the quality of the developed pattern recognition models. This makes the comparison among different approaches a challenging task. In addition, researchers can make mistakes that, when not detected, definitely affect the achieved results. To mitigate such issues, this paper proposes an open-source automatic and highly configurable framework, named B-HAR, for the definition, standardization, and development of a baseline framework in order to evaluate and compare HAR methodologies. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.

Chemotherapy-induced peripheral neurotoxicity (CIPN) diagnosis is largely based on patient reported outcomes. Wearables, sensors, and smart devices may potentially provide early detection and monitoring of CIPN. We systematically reviewed data on wearables, sensors, and smart devices to detect and/or monitor signs and symptoms of CIPN. Moreover, we provide directions and recommendations for future studies.A literature search using PubMed/MEDLINE, Web of Science, IEEE Xplore, and CINHAL databases was conducted from database inception until March 2021.The search was further updated in July 2022 to ensure currency of results. A total of 1885 records were title-abstract screened, 33 full texts were assessed, and 16 were included. The retrieved papers were heterogeneous in terms of study design, sample size, CIPN severity, chemotherapy agents, type of wearable/sensor/device applied, parameters of interest, and purpose. Data are promising and provide preliminary evidence on wearables, sensors, and smart devices for CIPN detection and monitoring.There are several issues and knowledge gaps that should be addressed. We propose a framework for future studies.

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