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Mode confusion occurs when the driver of an automated vehicle (AV) is confused about the active operating mode of the AV and therefore, their responsibilities as the driver. Mode confusion is a serious safety concern, especially for cohorts who are less familiar with AVs and/or who are more likely to have poorer situational awareness during automated driving such as older adults. In this article, we propose a design framework for driver state monitoring systems that can potentially be used to detect older drivers' mode confusion by inferring drivers' perceived AV mode using gaze behaviour data. As a proof-of-concept for an AV with two modes, the efficacy of the proposed framework is tested by applying it on a gaze behaviour dataset collected from 29 older drivers (65+) during simulated non-automated and simulated fully automated drives. The proposed framework utilizes classification models trained on features extracted from the gaze behaviour data. Among 25 features, the mRMR (maximum relevance minimum redundancy) feature ranking framework ranked our proposed feature of weighted static gaze entropy as having the highest relevance with the driver's perceived AV modes while having the least redundancy with the rest of the selected features. An ensemble stacking model achieved the highest classification performance with an average accuracy of 73% and an average AUC score of 80%. The results indicate that gaze behaviour features can distinguish between the driving scenarios of automated and non-automated as perceived by the drivers. While the dataset does not include confirmed instances of driver's mode confusion and therefore, the framework testing provides preliminary results towards a proof of concept, this work provides a foundational model for future studies in which actual data from confirmed mode confusions are intentionally introduced or measured. In turn, this study can inform future designs of driver state monitoring systems aimed to detect and mitigate the safety risks of driver's mode confusions in automated vehicles. [ABSTRACT FROM AUTHOR]

Background: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment. Objective: This review aims to evaluate existing studies on hand function evaluation using smartphones. Methods: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions. Results: In total, 46 studies were included. Overall, 11 types of hand dysfunction–related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation. Conclusions: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses. [ABSTRACT FROM AUTHOR]

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in 1-min windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data. [ABSTRACT FROM AUTHOR]