Wearables, sensors, and smart devices for the detection and monitoring of chemotherapy-induced peripheral neurotoxicity: Systematic review and directions for future research.

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.

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey.

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients.

Pain and physical function are both essential indices of recovery in critically ill patients in the Intensive Care Units (ICU). Simultaneous monitoring of pain intensity and patient activity can be important for determining which analgesic interventions can optimize mobility and function, while minimizing opioid harm. Nonetheless, so far, our knowledge of the relation between pain and activity has been limited to manual and sporadic activity assessments. In recent years, wearable devices equipped with 3-axis accelerometers have been used in many domains to provide a continuous and automated measure of mobility and physical activity. In this study, we collected activity intensity data from 57 ICU patients, using the Actigraph GT3X device. We also collected relevant clinical information, including nurse assessments of pain intensity, recorded every 1-4 hours. Our results show the joint distribution and state transition of joint activity and pain states in critically ill patients.

Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease.

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the gait. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state-of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG.