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A functional automatic test pattern generator (ATPG) that explores the design under test (DUT) state space by exploiting an easy-to-traverse extended finite state machine (FSM) model has been described. The ATPG engine relies on learning, backjumping and constraint logic programming to deterministically generate test vectors for traversing all transitions of the extended FSM. Testing of hard-to-detect faults is thus improved. The generated test sequences are very effective in detecting both high-level faults and gate-level stuck-at faults. Thus, the reuse of test sequences generated by the proposed ATPG allows also to improve the stuck-at fault coverage and to reduce the execution time of commercial gate-level ATPGs. [ABSTRACT FROM AUTHOR]

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., (© 2022 Peripheral Nerve Society.)

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.