Obstructive sleep apnea (OSA) is a respiratory disorder characterized by interruption to breathing during sleep. Usually, the OSA is more severe in the supine sleeping position. Recent studies also demonstrated that the head position may play an important role in the pathophysiology of the OSA. Therefore, monitoring the sleeping body and the head position has high clinical importance to optimize the treatment of the OSA. In this paper, three machine learning approaches were used to detect the head position during sleep in infrared images. In the first two methods, supervised classifiers were trained to estimate the head position based on different feature sets extracted from infrared images. In the third method, three different convolutional neural network (CNN) structures (ResNet50, MobileNet, and Darknet19) were trained to detect the head position during sleep. To detect the body position, the same CNN architectures were trained on infrared images. Overnight sleeping data (sleep duration = 5±1 h) from 50 participants (age: 53 ± 15 years, BMI: 29 ± 6 kg/m2, and 30 men/20 women) with various levels of OSA severity as measured by the apnea-hypopnea index (AHI = 25 ± 29 events/h and OSA severity: 12 normal, 13 mild, 11 moderate, and 14 severe) were collected for this paper. The models were trained on the data collected in one laboratory room from half of the participants and tested on the data from the other half collected in a different laboratory room. The best performing model (Darknet19) correctly estimated the lateral versus supine head position with 92% accuracy and 94% F1-Score and the lateral versus supine body position with 87% accuracy and 87% F1-Score. INDEX TERMS Computer vision, machine learning, position detection, sleep apnea, non-contact monitoring. The associate editor coordinating the review of this manuscript and approving it for publication was Thomas Penzel.

Background: Falls are a major health concern, with one in three adults over the age of 65 falling each year. A key gait parameter that is indicative of tripping is minimum foot clearance (MFC), which occurs during the mid-swing phase of gait. This is the second of a two-part scoping review on MFC literature. The aim of this paper is to identify vulnerable populations and conditions that impact MFC mean or median relative to controls. This information will inform future design/maintenance standards and outdoor built environment guidelines. Methods: Four electronic databases were searched to identify journal articles and conference papers that report level-ground MFC characteristics. Two independent reviewers screened papers for inclusion. Results: Out of 1571 papers, 43 relevant papers were included in this review. Twenty-eight conditions have been studied for effects on MFC. Eleven of the 28 conditions led to a decrease in mean or median MFC including dual-task walking in older adults, fallers with multiple sclerosis, and treadmill walking. All studies were conducted indoors. Conclusions: The lack of standardized research methods and covariates such as gait speed made it difficult to compare MFC values between studies for the purpose of defining design and maintenance standards for the outdoor built environment. Standardized methods for defining MFC and an emphasis on outdoor trials are needed in future studies.

Background: Falls are a major public health issue and tripping is the most common self-reported cause of outdoor falls. Minimum foot clearance (MFC) is a key parameter for identifying the probability of tripping. Optical motion capture systems are commonly used to measure MFC values; however, there is a need to identify alternative modalities that are better suited to collecting data in real-world settings. Objective: This is the first of a two-part scoping review. The objective of this paper is to identify and evaluate alternative measurement modalities to optical motion capture systems for measuring level-ground MFC. A companion paper identifies conditions that impact MFC and the range of MFC values individuals that these conditions exhibit. Methods: We searched four electronic databases, where peer-reviewed journals and conference papers reporting level-ground MFC characteristics were identified. The papers were screened by two independent reviewers for inclusion. The reporting was done in keeping with the PRISMA-ScR reporting guidelines. Results: From an initial search of 1571 papers, 17 papers were included in this paper. The identified technologies were inertial measurement units (IMUs) (n = 10), ultrasonic sensors (n = 2), infrared sensors (IR) (n = 2), optical proximity sensors (OPS) (n = 1), laser ranging sensors (n = 1), and ultra-wideband sensors (n = 1). From the papers, we extracted the sensor type, the analysis methods, the properties of the proposed system, and its accuracy and validation methods. Conclusions: The two most commonly used alternative modalities were IMUs and OPS. There was a lack of standardization among studies utilizing the same measurement modalities, as well as discrepancies in the methods used to assess performance. We provide a list of recommendations for future work to allow for more meaningful comparison between modalities as well as future research directions.

Background and Objectives Driving cessation is a complex challenge with significant emotional and health implications for people with dementia, which also affects their family care partners. Automated vehicles (AVs) could potentially be used to delay driving cessation and its adverse consequences for people with dementia and their care partners. Yet, no study to date has investigated whether care partners consider AVs to be potentially useful for people with dementia. Research Design and Methods This mixed-methods study assessed the views of 20 former or current family care partners of people with dementia on AV use by people with dementia. Specifically, questionnaires and semistructured interviews were used to examine care partners’ acceptance of AV use by people with dementia and their views about the potential usefulness of AVs for people with dementia. Results The results demonstrated that care partners identified possible benefits of AV use by people with dementia such as their anticipated higher social participation. However, care partners also voiced major concerns around AV use by people with dementia and reported significantly lower levels of trust in and perceived safety of AVs if used by the person with dementia in their care compared to themselves. Care partners’ concerns about AV use by people with dementia included concerns around the driving of people with dementia that AVs are not designed to address; concerns that are specific to AVs but are not relevant to the nonautomated driving of people with dementia; and concerns that arise from existing challenges around the nonautomated driving of people with dementia but may be exacerbated by AV use. Discussion and Implications Findings from this study can inform future designs of AVs that are more accessible and useful for people with dementia.

Background and Objectives The prospect of automated vehicles (AV) has generated excitement among the public and the research community about their potential to sustain the safe driving of people with dementia. However, no study to date has assessed the views of people with dementia on whether AVs may address their driving challenges. Research Design and Methods This mixed-methods study included two phases, completed by nine people with dementia. Phase I included questionnaires and individual semi-structured interviews on attitudes towards using different types of AVs (i.e., partially or fully automated). Interpretative phenomenological analysis was used to assess participants’ underlying reasons for and against AV use. The participants’ identified reasons against AV use informed the focus group discussions in Phase II, where participants were asked to reflect on potential means of overcoming their hesitancies regarding AV use. Results The results showed that people with dementia may place higher levels of trust in fully automated compared to partially automated AVs. In addition, while people with dementia expressed multiple incentives to use AVs (e.g., regaining personal freedom), they also had hesitations about AV use. These hesitancies were based on their perceptions about AVs (e.g., cost), their own abilities (i.e., potential challenges operating an AV), and driving conditions (i.e., risk of driving in adverse weather conditions). Discussion and Implications The findings of this study can help promote the research community’s appreciation and understanding of the significant potential of AVs for people with dementia while elucidating the potential barriers of AV use by people with dementia.

Over half of older adult falls are caused by tripping. Many of these trips are likely due to obstacles present on walkways that put older adults or other individuals with low foot clearance at risk. Yet, Minimum Foot Clearance (MFC) values have not been measured in real-world settings and existing methods make it difficult to do so. In this paper, we present the Minimum Foot Clearance Estimation (MFCE) system that includes a device for collecting calibrated video data from pedestrians on outdoor walkways and a computer vision algorithm for estimating MFC values for these individuals. This system is designed to be positioned at ground level next to a walkway to efficiently collect sagittal plane videos of many pedestrians’ feet, which is then processed offline to obtain MFC estimates. Five-hundred frames of video data collected from 50 different pedestrians was used to train (370 frames) and test (130 frames) a convolutional neural network. Finally, data from 10 pedestrians was analyzed manually by three raters and compared to the results of the network. The footwear detection network had an Intersection over Union of 85% and was able to find the bottom of a segmented shoe with a 3-pixel average error. Root Mean Squared (RMS) errors for the manual and automated methods for estimating MFC values were 2.32 mm, and 3.70 mm, respectively. Future work will compare the accuracy of the MFCE system to a gold standard motion capture system and the system will be used to estimate the distribution of MFC values for the population.

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Background The progression of dementia often leads to complete driving cessation, which poses major challenges for persons with dementia (PwD) and their caregivers. In response to these challenges, the use of Automated Vehicles (AV) by PwD has been considered as a way of prolonging PwD’s safe driving. AVs can either be used to assist PwD with certain driving tasks, such as steering or braking (Partially Automated Vehicles; PAVs), or by performing all driving tasks (Fully Automated Vehicles; FAVs). There are unique considerations regarding the use of AVs by PwD that are currently not well‐understood. This study examined caregivers’ perspective on the usefulness of AVs in addressing the driving‐related challenges faced by PwD. Method Semi‐structured interviews were conducted with 20 primary family caregivers of PwD. Both in the interviews and using a Likert scale questionnaire, participants were asked about their attitude towards PAV and FAV use by themselves and the PwD in their care. Thematic analysis with inductive coding was used to analyse the transcribed interview data. Result Caregivers reported significantly more negative attitudes towards PAV/FAV use by the PwD in their care compared to use by themselves (Table 1). The thematic analysis yielded two overarching types of caregiver concerns. (1) unresolved concerns about PwD’s mobility that persist after PAV/FAV use: difficulty navigating tasks at the destination; AVs not providing the same sense of freedom as driving; need for caregivers’ presence in the vehicle; caregivers’ unawareness of PwDs’ driving ability decline until a traffic incident. (2) emerging concerns specific to PAV/FAV use by PwD: PwD’s confusion caused by lack of exposure to AVs; PwD’s possible distress/agitation in AVs; PwD’s possible inability to navigate tasks required by the AV (e.g., response to system failures, negotiating pick‐up/drop‐off locations); PwD’s manual driving skill degradation upon constant use of AVs; AVs enabling PWD to wander to distant locations. Conclusion This study helps to identify AV design targets specific to PwD. In addition, study results outline caregivers’ concerns around AV use by PwD that extend beyond PwD’s driving, which highlights the importance of considering a holistic perspective when addressing mobility‐related needs of PwD by introducing AVs.