1
|
De Biase MEM, Alonso AC, da Silva RN, Soares SM, Canonica AC, Belini APDR, Soares-Junior JM, Baracat EC, Busse AL, Jacob-Filho W, Brech GC, Greve JMD. Multifactorial assessment of braking time predictors in a driving simulator among older adults according to gender. Clinics (Sao Paulo) 2024; 79:100405. [PMID: 38968666 DOI: 10.1016/j.clinsp.2024.100405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/17/2024] [Accepted: 05/20/2024] [Indexed: 07/07/2024] Open
Abstract
CONTEXT Vehicle driving depends on the integration of motor, visual, and cognitive skills to respond appropriately to different situations that occur in traffic. OBJECTIVES To analyze a model of performance predictor for braking time in the driving simulator, using a battery of tests divided by gender. METHODS Selected were 100 male drivers with a mean age of 72.6 ± 5.7 years. Sociodemographic variables, braking time in the driving simulator, and motor, visual, and cognitive skills were evaluated. RESULTS Comparing genders, men were older than women (p = 0.002) and had longer driving times (p = 0.001). Men had more strength in hand grip (p ≤ 0.001). In the linear regression analysis, the model explained 68 % of the braking time in men and 50.8 % in women. In the stepwise multiple linear regression analysis, the variable that remained in the model was the strength of the right plantar flexors, which explained 13 % of the braking time in women and men, and the cognitive variables explained 38.9 %. CONCLUSION Sociodemographic, motor, visual, and cognitive variables, explained a substantial portion of the variability in braking time for both older women and men, the specific variables driving this performance differed between the sexes. For older women, factors such as muscle strength emerged as critical determinants of braking ability, highlighting the importance of physical health in maintaining driving skills. On the other hand, cognitive conditions emerged as the primary predictor of braking performance in older men, underscoring the role of mental acuity and decision-making processes in safe driving.
Collapse
Affiliation(s)
- Maria Eugenia Mayr De Biase
- Laboratory Study of Movement, Instituto de Ortopedia e Traumatologia do Hospital das Clínicas (IOT-HC) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | - Angelica Castilho Alonso
- Laboratory Study of Movement, Instituto de Ortopedia e Traumatologia do Hospital das Clínicas (IOT-HC) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil; Graduate Program in Aging Sciences, Universidade São Judas Tadeu (USJT), São Paulo, SP, Brazil
| | | | - Sara Moutinho Soares
- Laboratory Study of Movement, Instituto de Ortopedia e Traumatologia do Hospital das Clínicas (IOT-HC) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | - Alexandra Carolina Canonica
- Laboratory Study of Movement, Instituto de Ortopedia e Traumatologia do Hospital das Clínicas (IOT-HC) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | | | - Jose Maria Soares-Junior
- Disciplina de Ginecologia, Departamento de Obstetrícia e Ginecologia, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | - Edmund Chada Baracat
- Disciplina de Ginecologia, Departamento de Obstetrícia e Ginecologia, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | - Alexandre Leopold Busse
- Departamento de Geriatria da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | - Wilson Jacob-Filho
- Departamento de Geriatria da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| | - Guilherme Carlos Brech
- Laboratory Study of Movement, Instituto de Ortopedia e Traumatologia do Hospital das Clínicas (IOT-HC) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil; Graduate Program in Aging Sciences, Universidade São Judas Tadeu (USJT), São Paulo, SP, Brazil.
| | - Júlia Maria D'Andrea Greve
- Laboratory Study of Movement, Instituto de Ortopedia e Traumatologia do Hospital das Clínicas (IOT-HC) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brazil
| |
Collapse
|
2
|
Scheutz M, Aeron S, Aygun A, de Ruiter JP, Fantini S, Fernandez C, Haga Z, Nguyen T, Lyu B. Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals. Top Cogn Sci 2024; 16:485-526. [PMID: 37389823 DOI: 10.1111/tops.12669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 07/01/2023]
Abstract
As human-machine teams are being considered for a variety of mixed-initiative tasks, detecting and being responsive to human cognitive states, in particular systematic cognitive states, is among the most critical capabilities for artificial systems to ensure smooth interactions with humans and high overall team performance. Various human physiological parameters, such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near-infrared spectroscopy or electroencephalogram, have been linked to different systemic cognitive states, such as workload, distraction, or mind-wandering among others. Whether these multimodal signals are indeed sufficient to isolate such cognitive states across individuals performing tasks or whether additional contextual information (e.g., about the task state or the task environment) is required for making appropriate inferences remains an important open problem. In this paper, we introduce an experimental and machine learning framework for investigating these questions and focus specifically on using physiological and neurophysiological measurements to learn classifiers associated with systemic cognitive states like cognitive load, distraction, sense of urgency, mind wandering, and interference. Specifically, we describe a multitasking interactive experimental setting used to obtain a comprehensive multimodal data set which provided the foundation for a first evaluation of various standard state-of-the-art machine learning techniques with respect to their effectiveness in inferring systemic cognitive states. While the classification success of these standard methods based on just the physiological and neurophysiological signals across subjects was modest, which is to be expected given the complexity of the classification problem and the possibility that higher accuracy rates might not in general be achievable, the results nevertheless can serve as a baseline for evaluating future efforts to improve classification, especially methods that take contextual aspects such as task and environmental states into account.
Collapse
Affiliation(s)
| | - Shuchin Aeron
- Department of Electrical and Computer Engineering, Tufts University
| | - Ayca Aygun
- Department of Computer Science, Tufts University
| | - J P de Ruiter
- Department of Computer Science, Tufts University
- Department of Psychology, Tufts University
| | | | | | - Zachary Haga
- Department of Computer Science, Tufts University
| | - Thuan Nguyen
- Department of Computer Science, Tufts University
| | - Boyang Lyu
- Department of Electrical and Computer Engineering, Tufts University
| |
Collapse
|
3
|
Park J, Zahabi M, Zheng X, Ory M, Benden M, McDonald AD, Li W. Automated vehicles for older adults with cognitive impairment: a survey study. ERGONOMICS 2024; 67:831-848. [PMID: 38226633 DOI: 10.1080/00140139.2024.2302020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/02/2024] [Indexed: 01/17/2024]
Abstract
As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.
Collapse
Affiliation(s)
- Junho Park
- Department of General Engineering, Santa Clara University, Santa Clara, CA, USA
| | - Maryam Zahabi
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | | | - Marcia Ory
- School of Public Health, Texas A&M University, College Station, TX, USA
| | - Mark Benden
- School of Public Health, Texas A&M University, College Station, TX, USA
| | - Anthony D McDonald
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Wei Li
- Department of Landscape Architecture and Urban Planning, School of Architecture, Texas A&M University, College Station, TX, USA
| |
Collapse
|
4
|
Model Lightweighting for Real-time Distraction Detection on Resource-Limited Devices. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7360170. [PMID: 36590846 PMCID: PMC9803561 DOI: 10.1155/2022/7360170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/14/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022]
Abstract
Detecting distracted driving accurately and quickly with limited resources is an essential yet underexplored problem. Most of the existing works ignore the resource-limited reality. In this work, we aim to achieve accurate and fast distracted driver detection in the context of embedded devices where only limited memory and computing resources are available. Specifically, we propose a novel convolutional neural network (CNN) light-weighting method via adjusting block layers and shrinking network channels without compromising the model's accuracy. Finally, the model is deployed on multiple devices with real-time detection of driving behaviour. The experimental results for the American University in Cairo (AUC) and StateFarm datasets demonstrate the effectiveness of the proposed method. For instance, for the AUC dataset, the proposed MobileNetV2-tiny model achieves 1.63% higher accuracy with just 78% of the model parameters of the original MobileNetV2 model. The inference speed of the proposed MobileNetV2-tiny model on resource-limited devices is on average 1.5 times that of the original MobileNetV2 model, which can meet real-time requirements.
Collapse
|
5
|
Ye C, Li W, Li Z, Maguluri G, Grimble J, Bonatt J, Miske J, Iftimia N, Lin S, Grimm M. Smart Steering Sleeve (S 3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197296. [PMID: 36236395 PMCID: PMC9573431 DOI: 10.3390/s22197296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 05/14/2023]
Abstract
Driving is a ubiquitous activity that requires both motor skills and cognitive focus. These aspects become more problematic for some seniors, who have underlining medical conditions and tend to lose some of these capabilities. Therefore, driving can be used as a controlled environment for the frequent, non-intrusive monitoring of bio-physical and cognitive status within drivers. Such information can then be utilized for enhanced assistive vehicle controls and/or driver health monitoring. In this paper, we present a novel multi-modal smart steering sleeve (S3) system with an integrated sensing platform that can non-intrusively and continuously measure a driver's physiological signals, including electrodermal activity (EDA), electromyography (EMG), and hand pressure. The sensor suite was developed by combining low-cost interdigitated electrodes with a piezoresistive force sensor on a single, flexible polymer substrate. Comprehensive characterizations on the sensing modalities were performed with promising results demonstrated. The sweat-sensing unit (SSU) for EDA monitoring works under a 100 Hz alternative current (AC) source. The EMG signal acquired by the EMG-sensing unit (EMGSU) was amplified to within 5 V. The force-sensing unit (FSU) for hand pressure detection has a range of 25 N. This flexible sensor was mounted on an off-the-shelf steering wheel sleeve, making it an add-on system that can be installed on any existing vehicles for convenient and wide-coverage driver monitoring. A cloud-based communication scheme was developed for the ease of data collection and analysis. Sensing platform development, performance, and limitations, as well as other potential applications, are discussed in detail in this paper.
Collapse
Affiliation(s)
- Chuwei Ye
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Wen Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Zhaojian Li
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
- Correspondence:
| | | | | | | | - Jacob Miske
- Physical Sciences Inc., Boston, MA 01810, USA
| | | | - Shaoting Lin
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Michele Grimm
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
| |
Collapse
|
6
|
Hussein A, Ghignone L, Nguyen T, Salimi N, Nguyen H, Wang M, Abbass HA. Characterization of Indicators for Adaptive Human-Swarm Teaming. Front Robot AI 2022; 9:745958. [PMID: 35252363 PMCID: PMC8891141 DOI: 10.3389/frobt.2022.745958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 01/12/2022] [Indexed: 12/23/2022] Open
Abstract
Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.
Collapse
|
7
|
Yu RWL, Chan AHS. Meta-analysis of the effects of game types and devices on older adults-video game interaction: Implications for video game training on cognition. APPLIED ERGONOMICS 2021; 96:103477. [PMID: 34107433 DOI: 10.1016/j.apergo.2021.103477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 06/12/2023]
Abstract
Video game training can effectively improve the cognition of older adults. However, whether video game types and game devices influence the training effects of video games remains controversial. This meta-analysis aimed to access and evaluate the effects of video game types and game devices in video game training on the cognition of older adults. Interestingly, results indicated that mouse/keyboard was superior over other video game devices on perceptual-motor function. The effect size (Hedge's g) for perceptual-motor function decreased by 1.777 and 1.722 when the video game training device changed from mouse/keyboard to driving simulator and motion controller. The effects of cognitive training game and conventional video game were moderated by session length. More well-designed studies are required to clarify the unique efficacy of video game types and devices for older adults with video game training.
Collapse
Affiliation(s)
- Rita Wing Lam Yu
- Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong.
| | - Alan Hoi Shou Chan
- Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong.
| |
Collapse
|
8
|
Impact of Disabled Driver’s Mass Center Location on Biomechanical Parameters during Crash. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041427] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Adapting a car for a disable person involves adding additional equipment to compensate for the driver’s disability. During this process, the change in the driver’s position and kinematics and their impact on safety levels during crash is not considered. There is also a lack of studies in the literature on this problem. This paper describes a methodology for conducting a study of the behavior of a disabled driver during a crash using the finite element method, based on an explicit time integration method. A validated car model and a commercial dummy model were used. The results show that the use of a handle on the steering wheel and a hand control unit causes dangerous lateral displacements relative to the seat. Amputation of the left leg or right arm causes significant shoulder rotations, amputation of the left leg causes increased thoracic loads. Amputation or additional equipment have no significant impact on head injuries.
Collapse
|