Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

Rapid nanomolar detection of cocaine in biofluids by electrochemical aptamer-based sensor with low-temperature effect for drugged driving screening.

Cocaine is one of the most abused illicit drugs, and its abuse damages the central nervous system and can even lead directly to death. Therefore, the development of simple, rapid and highly sensitive detection methods is crucial for the prevention and control of drug abuse, traffic accidents and crime. In this work, an electrochemical aptamer-based (EAB) sensor based on the low-temperature enhancement effect was developed for the direct determination of cocaine in bio-samples. The signal gain of the sensor at 10 °C was greatly improved compared to room temperature, owing to the improved affinity between the aptamer and the target. Additionally, the electroactive area of the gold electrode used to fabricate the EAB sensor was increased 20 times by a simple electrochemical roughening method. The porous electrode possesses more efficient electron transfer and better antifouling properties after roughening. These improvements enabled the sensor to achieve rapid detection of cocaine in complex bio-samples. The low detection limits (LOD) of cocaine in undiluted urine, 50% serum and 50% saliva were 70 nM, 30 nM and 10 nM, respectively, which are below the concentration threshold in drugged driving screening. The aptasensor was simple to construct and reusable, which offers potential for drugged driving screening in the real world.

Lived experience of driving in individuals with functional neurological disorder.

BACKGROUND: Functional neurological disorder (FND) is a common neurological diagnosis that encapsulates a range of incapacitating clinical presentations. These include functional seizures, movement disorders, and sensory disturbances. Safe driving requires both cognitive skills and physical abilities, which may be impacted by FND symptoms. The primary objective of this study was to gain deeper insights into the challenges faced by people with FND when driving. METHODS: A qualitative study and interpretative phenomenological analysis were conducted. Individuals experiencing functional seizures and/or movement disorders completed both questionnaires and semi-structured interviews about FND symptoms, driving behavior, and crashes. RESULTS: A total of 26 patients with FND participated in this study. Based on the interviews, four key themes were identified: (1) driving difficulties experienced by individuals with FND; (2) strategies utilized by people with FND to overcome difficulties experienced while driving; (3) barriers preventing driving challenges being addressed in this population; and (4) crashes and perceived dangerous driving events experienced by individuals with FND. All participants reported that driving a car provoked FND symptoms and this affected their driving ability. FND sufferers reported using a number of strategies such as limiting how far they drive and relying on advanced driver assistance system features to help manage their associated symptoms, such as fatigue and/or pain. Several participants reported crashes and perceived dangerous driving events since developing FND. CONCLUSION: Individuals experiencing FND often employ self-regulation techniques, yet the extent to which these methods enhance driving safety remains uncertain. The variable nature of the disorder makes judging an individual's driving risk particularly difficult. The themes emerging from the interviews highlighted the need for further empirical research to inform guidelines and best practice when determining the impact of FND on an individual's driving safety .

Evaluation of Driver's Brain Activity Associated with Advanced Driving Skills Such as Cornering.

Evaluating the driving ability of a vehicle is important in the development of in-vehicle systems and the training of driving skills. Driving ability has been investigated extensively in terms of recognition, judgment, and operation. However, the role of the brain in advanced driving operations within the limits of vehicle performance has not been thoroughly investigated. In this study, we perform functional magnetic resonance imaging to evaluate brain functions associated with advanced driving skills when drivers are shown a video of cornering involving a vehicle slipping sideways. Based on the results, the skilled driver group indicates broad activity in both the right and left parietal associations, right-side primary somatosensory, left-side premotor, and supplementary motor areas. Because the premotor cortex is a region involved in the execution of movement, whereas the supplementary motor cortex is a region involved in spontaneous movement, it is assumed that the skilled drivers visualized the driving operation, and that the brain functions necessary for the operation are activated. These findings indicate that drivers with high skill levels exhibit distinctive brain activities. We believe that a further understanding regarding the brains of skilled drivers will facilitate the development of in-vehicle control that incorporates high driving skills and training.

Study on ring-road incident duration based on latent class accelerated hazard model.

Accurately estimating the duration of freeway incidents can enhance emergency management practices and reduce the likelihood of secondary incidents. To investigate the mechanisms through which key factors influence incident duration, this study sorted out the characteristics and variables of the incident duration on a special freeway in Zhejiang Province, that is, the ring road, and developed a latent class accelerated hazard model. Heterogeneity was incorporated into the model. Three distributions (Weibull, Log-normal, and Log-logistic) were compared, and the Log-logistic distribution exhibited superior performance. The analysis revealed two distinct latent classes: Latent Class 1 and Class 2, had class membership probability of 0.53 and 0.47, respectively, with a total of 11 variables being statistically significant at the 0.05 significance level. It is worth noting that, some neglected explanatory variables are discussed in depth in this study. For example, the mechanism of which specific lane is closed has an impact on the incident duration, rather than a general discussion of the number of lane closures. Furthermore, the way in which the driver involved in the incident reports to the police has a significant impact on the duration of incidents. Notably, potential heterogeneity and its influencing mechanism are captured in the model. Additionally, by predicting class membership using posterior probabilities, it was determined that most data points were more likely to belong to Class 1, and the incident duration primarily ranged between 0 and 60 minutes. These findings are helpful to reduce the duration of incidents on ring-roads and freeways in China, and provide theoretical support for the formulation of freeway incident management and treatment policies.

Establishing the Predictive Validity of the Assessment of Motor and Process Skills for Driving Performance Outcomes.

IMPORTANCE: Although the Assessment of Motor and Process Skills (AMPS) is an excellent tool for evaluating the functional performance of instrumental activities of daily living (IADLs), a limited number of studies have used the AMPS for decisions regarding the IADL of fitness to drive and community mobility. OBJECTIVE: To determine the specificity and sensitivity of the AMPS as a tool for determining a person's fitness to drive. DESIGN: Cross-sectional observational design. SETTING: Three driving rehabilitation programs in three states. PARTICIPANTS: Participants were 388 community-living adults (M age = 68.74 yr, SD = 11.53); 196 adults were recruited before completing a comprehensive driving evaluation, and 192 were recruited in two other studies of older drivers. OUTCOME AND MEASURES: AMPS and results of comprehensive driving evaluation or on-road assessment. RESULTS: Using a logistical regression, AMPS Motor and Process Skills scores yielded a sensitivity of 84.6% and a specificity of 88.8%. The odds ratio of the AMPS Motor Skills score was .347; for the AMPS Process Skills score, it was .014. Using cross-validations, the model with AMPS Motor and Process scores produced a cross-validation area under the curve of .918, with sensitivity and specificity of 84.6% and 88.4%, respectively, and a probability greater than .334 was used for predicting a fail or drive-with-restriction evaluation. CONCLUSIONS AND RELEVANCE: The AMPS Motor and Process Skills scores revealed significant differences between those who failed or had driving restrictions and with those who passed the driving evaluation, which supported the AMPS as an effective tool for predicting fitness to drive. Plain-Language Summary: This study demonstrates how the Assessment of Motor and Process Skills (AMPS), as a top-down occupational therapy assessment tool, can be used to differentiate between medically at-risk drivers who are likely to pass a comprehensive driving evaluation and those who are likely to fail or need restrictions. AMPS will assist occupational therapy practitioners in determining who is most appropriate to receive driving rehabilitation services and/or when to refer a person for a comprehensive driving evaluation.

Evaluation of the effectiveness of three different interventions on older driver safety over a 12-month period: study protocol for a randomised controlled trial.

INTRODUCTION: The growing population of older drivers presents challenges for road safety attributed to age-related declines and increased crash fatality rates. However, enabling older people to maintain their health and independence through continued safe driving is important. This study focuses on the urgent need for cost-effective interventions that reduce crash risk while supporting older drivers to remain driving safely for longer. Our study aims to evaluate the effectiveness of three behavioural interventions for older driver safety. These include an online road-rules refresher workshop, tailored feedback on driving performance and two tailored driving lessons. METHODS AND ANALYSIS: A single-blind three-parallel group superiority randomised controlled trial will be conducted with 198 urban licensed drivers aged 65 years and older, allowing for 4% attrition. This sample size provides 80% power to detect a difference with an alpha of 0.05. Participants will be selected based on a standardised on-road test that identifies them as moderately unsafe drivers. Interventions, spanning a 3-month period, aim to improve driving safety. Their effectiveness will be assessed through a standardised on-road assessment of driving safety at 3 months (T1) and 12 months postintervention (T2). Additionally, monthly self-reported driving diaries will provide data on crashes and incidents.This trial has the potential to identify cost-effective approaches for improving safety for older drivers and contribute to evidence-based health policy, clinical practice and guidelines. ETHICS AND DISSEMINATION: Ethical approval was obtained by the University of New South Wales Human Research Ethics Committee (HC190439, 22 August 2019). The results of the study will be disseminated in peer-reviewed journals and research conferences. TRIAL REGISTRATION NUMBER: ACTRN12622001515785.

Illumination Intelligent Adaptation and Analysis Framework: A comprehensive solution for enhancing nighttime driving fatigue monitoring.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.

The architecture of functional brain network modulated by driving under train running noise exposure.

A noisy environment can considerably impact drivers' attention and fatigue, endangering driving safety. Consequently, this study designed a simulated driving experimental scenario to analyse the effects of noise generated during urban rail transit train operation on drivers' functional brain networks. The experiment recruited 16 participants, and the simulated driving scenario was conducted at noise levels of 50, 60, 70, and 80 dB. Functional connectivity between all electrode pairs across various frequency bands was evaluated using the weighted phase lag index (WPLI), and a brain network based on this was constructed. Graph theoretic analysis employed network global efficiency, degree, and clustering coefficient as metrics. Significant increases in the WPLI values of theta and alpha frequency bands were observed in high noise environments (70 dB, 80 dB), as well as enhanced brain synchronisation. Furthermore, concerning the topological metrics of brain networks, it was observed that the global efficiency of brain networks in theta and alpha frequency ranges, as well as the node degree and clustering coefficients, experienced substantial growth in high noise environments (70 dB, 80 dB) as opposed to 50 dB and 60 dB. This finding indicates that high-noise environments impact the reorganisation of functional brain networks, leading to a preference for network structures with improved global efficiency. Such findings may improve our understanding of the neural mechanisms of driving under noise exposure, and thus potentially reduce road accidents to some extent.

Young onset dementia and driving cessation: a scoping review of lived experiences.

BACKGROUND: Driving cessation is one of the most challenging life transitions, associated with multiple negative consequences for individuals living with late-onset dementia. This paper extends the literature as to date there is no published review that details the experiences of people living with young onset dementia ("YOD"). METHODS: A comprehensive search of the literature was conducted using the scoping review methodology. RESULTS: Ten studies were included for full text review of 1634 initially identified through database searching. The results of the included articles indicated areas of concern for people living with YOD and their family members including, loss of independence; role change; threat to self-identify; feelings of isolation, grief; acceptance; predictors of driving cessation. CONCLUSION: There is a lack of robust evidence related to driving cessation and the experiences of people living with YOD. No published paper reported psychosocial interventions specifically targeted at supporting persons with YOD through driving cessation.

Near miss road traffic accidents and associated factors among truck drivers in Gamo zone, southern Ethiopia by using a contributory factors interaction model.

Background: Road traffic accidents (RTAs) are among the leading causes of injuries, fatalities, and the resulting increase in financial burdens worldwide. Every year, RTAs cause numerous serious injuries and fatalities in Ethiopia. it is important to understand how prevalent near-miss crash accidents are, and which by definition could have injured the victim but did not result in an actual accident. The determinants of these near-misses are essential in road crash accident reduction strategies. In spite of the fact that near-miss accidents are much more common than actual losses or injuries, very little research has been conducted on them. Thus, this study was intended to assess the near-miss accidents and associated factors among truckers in Gamo zone, southern Ethiopia. Methodology: The community-based cross-sectional study was employed from May 12 to July 10,2022, using a structured interviewer-administered questionnaire. A simple random sampling technique was used to select participants. The data were analyzed using the statistical package for social sciences. A binary and multivariate logistic regression model was used to identify the determinants of near-miss accidents. A statistical significance level was set at p < 0.05. Results: About 72.5% of truckers had experienced near-miss road traffic accidents. The majority of the near-miss accidents were caused by speeding, followed by driving on the wrong side of the road and skidding, 65 (22.6%), 39 (13.5%), and 38 (13.2%), respectively. Driving frequency per week, location of accidents, condition of the road, sleeping status, and weather conditions were significantly associated with near-miss accidents. Conclusion: The prevalence of near-miss accidents is high in the Gamo zone. Being a younger and less educated driver, high driving frequency per week, driving on major roads and junctions, foggy weather, and inadequate sleep all contribute to the occurrence of accidents. Road safety measures that could address these identified factors are required to mitigate potential RTAs.

Prevalence of Forward head posture among car and bike drivers and its relation with neck and cardiopulmonary health parameters- a cross-sectional study.

OBJECTIVES: This study aimed to evaluate and compare the prevalence of Forward Head Posture (FHP) in car and bike drivers, and its potential correlation with neck and cardiopulmonary parameters. METHODS: This cross-sectional study involved 400 participants from urban and suburban areas around Lucknow, Uttar Pradesh, India, including 200 car drivers and 200 bike drivers aged 18-65 years with a minimum five-year driving history. Neck health was assessed using measurements such as cervical range of motion and Neck Disability Index (NDI), cardiopulmonary parameters were evaluated through resting heart rate, blood pressure, and pulmonary function tests using the spirometry test, and FHP was assessed using Surgimap application. Statistical analysis was performed using IBM SPSS Statistics software (version 26.0) and included descriptive statistics, hypothesis testing, Chi-square or Fisher's exact test for binary data, and correlation analyses. RESULTS: The result show that difference in the mean FHP between car and bike drivers was statistically significant (p = 0.0001), indicating a higher prevalence of FHP among car drivers than among bike drivers. Correlation analyses revealed significant associations between FHP and neck health metrics, especially cervical flexion (r = 0.71, p<0.05), (r = 0.78, p<0.05) and left-side rotation (r = 0.56, p<0.05), (r = 0.61, p<0.05) in car and bike drivers. Among the cardiopulmonary parameters, significant correlations with FHP were observed in resting heart rate (r = 0.33, p<0.05), (r = 0.42, p<0.05), spirometry results FVC (r = 0.29, p<0.05), FEV1 (r = 0.22, p<0.05), and FVC (r = 0.31, p<0.05) for car and bike drivers. CONCLUSION: We observed a higher incidence of FHP in car drivers, indicating that a prolonged static posture may lead to greater postural deviation than dynamic movement during biking. This association suggests that FHP could have wide-reaching implications for systemic health, beyond musculoskeletal issues. These findings have the potential to influence preventative strategies and interventions aimed at improving the overall health outcomes for drivers.

Prospective control of steering through multiple waypoints.

Some locomotor tasks involve steering at high speeds through multiple waypoints within cluttered environments. Although in principle actors could treat each individual waypoint in isolation, skillful performance would seem to require them to adapt their trajectory to the most immediate waypoint in anticipation of subsequent waypoints. To date, there have been few studies of such behavior, and the evidence that does exist is inconclusive about whether steering is affected by multiple future waypoints. The present study was designed to address the need for a clearer understanding of how humans adapt their steering movements in anticipation of future goals. Subjects performed a simulated drone flying task in a forest-like virtual environment that was presented on a monitor while their eye movements were tracked. They were instructed to steer through a series of gates while the distance at which gates first became visible (i.e., lookahead distance) was manipulated between trials. When gates became visible at least 1-1/2 segments in advance, subjects successfully flew through a high percentage of gates, rarely collided with obstacles, and maintained a consistent speed. They also approached the most immediate gate in a way that depended on the angular position of the subsequent gate. However, when the lookahead distance was less than 1-1/2 segments, subjects followed longer paths and flew at slower, more variable speeds. The findings demonstrate that the control of steering through multiple waypoints does indeed depend on information from beyond the most immediate waypoint. Discussion focuses on the possible control strategies for steering through multiple waypoints.

Personalizing driver safety interfaces via driver cognitive factors inference.

Recent advances in AI and intelligent vehicle technology hold the promise of revolutionizing mobility and transportation through advanced driver assistance systems (ADAS). Certain cognitive factors, such as impulsivity and inhibitory control have been shown to relate to risky driving behavior and on-road risk-taking. However, existing systems fail to leverage such factors in assistive driving technologies adequately. Varying the levels of these cognitive factors could influence the effectiveness and acceptance of ADAS interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver's latent cognitive states from their driving behavior. To accomplish this, we adopt a data-driven approach and train a recurrent neural network to infer impulsivity and inhibitory control from recent driving behavior. The network is trained on a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using data collected from a high-fidelity vehicle motion simulator experiment, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to determine instantly whether or not to engage a driver safety interface. This approach was evaluated using leave-one-out cross validation using actual human data. Our evaluations reveal that our personalized driver safety interface that captures the cognitive profile of the driver is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.

Traffic jam by GPS: A systematic analysis of the negative social externalities of large-scale navigation technologies.

The increased usage of navigation technologies has caused conflicts in local traffic management, resulting in congested residential areas among other challenges for residents. This paper uses content analysis to investigate such negative social externalities within local communities and neighbourhoods. Through a corpus of 90 news articles about traffic incidents caused by navigation technologies, we identified negative traffic and safety-related externalities, including congestion, damage, pollution, and accidents. We also report on countermeasures by local communities and governments, including street closures, speed limit reduction, and turn bans. Based on our results, we discuss the implications for designing mobile navigation technologies that reduce negative social externalities.

Musculoskeletal disorders among taxi drivers: a systematic review and meta-analysis.

INTRODUCTION: Taxi drivers, as professional drivers, encounter numerous ergonomic risk factors related to musculoskeletal disorders (MSDs) because of the demands of their jobs. This study conducted as a systematic review and meta-analysis aimed to explore the prevalence of MSDs among taxi drivers. MATERIALS AND METHODS: The present study followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, and its protocol was registered in the international prospective register of systematic review (PROSPERO) under the code CRD42024509258. Searches were carried out using various databases, such as PubMed, Scopus, Web of Science, Science Direct, SID, ISC, and Google Scholar, with no time restrictions until February 7th, 2024. A random effects model was utilized for meta-analysis, and the I2 index was employed to assess heterogeneity among studies. Lastly, data analysis was conducted using STATA software (version 14). RESULTS: After the initial search, 1606 articles were extracted from the reviewed sources. Following screening, study selection, and quality evaluation, a total of 11 studies were chosen for meta-analysis, involving 5277 taxi drivers. Based on the results of the meta-analysis, the highest prevalence of MSDs among taxi drivers was related to the lower back region (53.87% (95% CI:40.89-66.84, I2= 98.7%, P < 0.001)). Additionally, the prevalence rates of MSDs in different body regions, such as the neck (38.15%), shoulder (34.97%), upper back (18.30%), and knee (14.10%), were also reported. CONCLUSION: Based on the findings of this study, the prevalence of MSDs among taxi drivers is relatively high, and specific risk factors may contribute to the development of these disorders. Therefore, to prevent the occurrence of MSDs among taxi drivers, it is advisable to implement essential measures concerning the development of training programs, ergonomic interventions, and evaluation of the work environment.

Toward Safe and Confident Silver Drivers: Interview Study Investigating Older Adults' Driving Practices.

BACKGROUND: As the aging population in the United States continues to increase rapidly, preserving the mobility and independence of older adults becomes increasingly critical for enabling aging in place successfully. While personal vehicular transport remains a popular choice among this demographic due to its provision of independence and control over their lives, age-related changes may heighten the risk of common driving errors and diminish driving abilities. OBJECTIVE: This study aims to investigate the driving practices of older adults and their efforts to maintain safe and confident driving habits. Specifically, we sought to identify the factors that positively and negatively influence older adults' driving performance and confidence, as well as the existing efforts put into sustaining their driving abilities. METHODS: We recruited 20 adults aged ≥65 years who remained active drivers during the recruitment from the greater New York area. Then, we conducted semistructured interviews with them to examine their perceptions, needs, and challenges regarding safe and confident driving. RESULTS: Our findings uncovered a notable disparity between older adults' self-perceived driving skills and the challenges they face, particularly caused by age-related limitations and health conditions such as vision and memory declines and medication routines. Drawing on these findings, we proposed strategies to bridge this gap and empower older adults to drive safely and confidently, including fostering a realistic understanding of their capabilities, encouraging open dialogue regarding their driving, encouraging regular assessments, and increasing awareness of available resources. CONCLUSIONS: This study uncovered a noticeable disparity between the perceived driving competence of older adults and the actual challenges they confront while driving. This divergence underscores a significant need for better support beyond the existing aid available to preserve older adults' driving skills. We hope that our recommendations will offer valuable insights for practitioners and scholars committed to enhancing the overall well-being and quality of life for older adults as they age in their homes.

Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion.

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.