Manual Distraction Research Articles

Physical Therapy Management for a Patient with Hallux Rigidus in a Baseball Player.

Hallux rigidus is a common condition affecting the foot. There is a paucity of evidence describing the management of patients with hallux rigidus with manual physical therapy consisting of hands-on manual therapy techniques and movement reinforcing exercise. This case highlights the management of a patient with hallux rigidus by a physical therapist. The patient was a 60-year-old male baseball player with pain, loss of metatarsophalangeal joint motion, and radiographically visible degenerative changes suggesting a diagnosis of hallux rigidus. Treatment involved non-thrust joint manual distraction mobilization to the 1st metatarsophalangeal joint. Improvements were noted immediately in the patient's ability to run with decreased symptoms. I carefully instructed the patient to perform the manual distraction techniques at home. After 4 clinical visits, the patient returned to baseball the following spring. Outcomes were maintained 8 years after initial evaluation.

CT Arthrography With Traction in Femoro-Acetabular Impingement: How to Do It?

Computed tomography arthrography (CTA) highly correlates with arthroscopy in detecting labral tears, especially in patients with positive impingement tests. CTA enables the acquisition of isotropic datasets with high spatial resolution within a single acquisition. However, the hip is a close-fitting, congruent, and nearly spherical joint, with relatively thin cartilage. We describe herein a simple and cost-effective method using traction for hip CTA, with axial manual distraction, that helps overcome the usual limitations, by widening the articular joint space and thus better delineating both acetabular and femoral cartilages.

Does it deliver what it promises? Evaluation of cognitive distraction caused by speech-based interfaces with detection response and box task

Speech is considered a promising modality for human-machine interaction while driving, especially in reducing visual and manual distraction. However, speech-based user interfaces themselves have shown to increase cognitive distraction. There remains a lack of standardized and unambiguous methods for measuring the impact of speech-based assistants on cognitive distraction while driving. This work aims to investigate whether the combination of the box task and the detection response task (DRT) is a suitable method for assessing the cognitive distraction caused by speech-based assistants. For this purpose, participants (N = 39) engaged in artificial (n-back tasks) and natural speech-based secondary tasks (interaction with Android’s Google Assistant and Apple's Siri) differing in predefined levels of cognitive workload while performing the box task and the DRT. The results showed that DRT performance differed between the 0-back and 1-back task but not between the different cognitive workload levels of the speech-based assistants. No clear effects emerged for the box task parameters. Thus, the combination of the box task and DRT is well-suited for measuring cognitive distraction caused by artificial secondary tasks but not by natural interactions with speech-based assistants.

Visualization of the distal tibial plafond articular surface using four established approaches and the efficacy of instrumented distraction: a cadaveric study

PurposeDirect visualization is a very effective method in accomplishing adequate articular surface reconstruction in fracture repair. This study investigates distal tibial plafond articular surface visibility using the anteromedial, anterolateral, posteromedial, and posterolateral approaches, the effect of instrumented distraction on visibility, and which zones of the articular surface are visible for each approach.MethodsThe anteromedial, anterolateral, posteromedial, and posterolateral approaches to the distal tibial plafond were performed on 16 cadaveric ankle specimens. The articular surface visualization for each approach was marked using an electrocautery device with manual and instrumented distraction. Articular surface visualization was photographically documented. Digital axial segmentation and quantitative analysis of the visualized distal tibial plafond articular surface were performed.ResultsWith manual distraction, distal tibial plafond articular surface visualization, expressed in percent of overall articular surface, was limited to 9% (SD ± 9) for the anteromedial, 24% (SD ± 18) for the anterolateral, 26% (SD ± 10) for the posteromedial, and 30% (SD ± 18) for the posterolateral approaches. Using instrumented distraction significantly improved articular surface visualization in all instances (p < 0.001). The anteromedial approach visible articular surface increased to 63% (SD ± 13), the anterolateral to 72% (SD ± 22), the posteromedial to 62% (SD ± 11), and the posterolateral to 50% (± 17).ConclusionThis study demonstrates the efficacy of instrumented distraction when attempting surgical visualization of the distal tibial plafond articular surface. Knowledge of approach specific articular surface visibility may assist the surgeon in choosing the appropriate approach(es) based on case-specific distal tibial plafond fracture patterns.Level of evidenceIV, cadaver study.

A survey on vision-based driver distraction analysis

Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as well as tremendous economic and societal costs. Driver inattention, either distraction or fatigue, is the major cause among human factors responsible for the crashes. Distracted driving has been getting increasingly severe, and has caused many more crashes than drowsy driving, while the latter has been more extensively studied. Therefore, we are motivated to present a comprehensive survey on vision-based approaches for driver distraction analysis. In the paper, we firstly provide an overview of driver distraction, then introduce the available datasets and explore the various cues for driver behavior distraction analysis. After that, two forms of driver behavior distraction (visual distraction and manual distraction) are analyzed separately. Lastly, we conclude the evolvement and future directions of driver distraction analysis for safe driving. To the best of our knowledge, we are the first to propose driver behavior distraction and analyze it in a hierarchical way.

Detection of Driver’s Distractedness Using Convolutional Neural Networks

Deaths caused due to Road Accidents has always been an area of grave concern and happens to be one of the most critical problems in India as on date. The inattentiveness of the driver causes almost 80% of the casualties. Utilization of mobile phones, talking to passengers, reaching behind to grab something, and drinking while driving are some reasons drivers may lose attention. Distractions are considered of numerous types, out of which we focus on the manual distraction, which is based on the driver's posture. In particular, we consider nine distinct distracted states of the driver. Then, our goal is to detect whether a given image of the driver falls into one of these categories. Eventually, we will integrate an alarm that will alert the driver if she/he is detected to be in a distracted state. Accordingly, this paper presents a mechanism where we use convolutional neural networks; a deep learning technique, to classify driver images. The image dataset we use to train and test our neural network consists of the first dataset made available publicly at the Kaggle data source.

Detecting Safe and Not Safe Driving Actions using Convolutional Neural Network

&lt;p&gt;The main cause of accidents is due to Manual, Visual or Cognitive distraction out of these three Manual distractions are concerned with various activities where “driver’s hands are off the wheel”. Such distractions include talking or texting using mobile phones, eating and drinking, talking to passengers in the vehicle, adjusting the radio, makeup, etc. To solve the problem of manual distraction, the Convolutional Neural Network (CNN) model of ResNet-50 using transfer learning with 23,587,712 parameters was used. The dataset used is from State Farm Distracted Driver Detection Dataset. The training accuracy is 97.27% and validation accuracy is 55%. Further the model works on detecting real-time distractions on a video feed for this purpose the system uses OpenCV and the model is integrated with the frontend using the flask.&lt;/p&gt;

Is organized carpooling safer? Speeding and distracted driving behaviors from a naturalistic driving study in Brazil

Carpooling consists of drivers and passengers sharing a journey and its costs. Nowadays, in the context of mobility as a service, organized carpooling encompasses a service and trust relationship between drivers and passengers, by matching common routes and splitting cost through mobile phone applications. Therefore, passengers expect a certain level of travel quality and safety. In this context, this research aims to verify the hypothesis that drivers in an organized carpooling situation (CP) show safer driving behavior in terms of speeding (SP) and mobile phone use while driving (MPU) in comparison with non-carpooling (NCP) drivers. The research is based on data from the Brazilian Naturalistic Driving Study (NDS-BR) conducted in the City of Curitiba, with 40.45 driving hours and a traveled distance of 895.87 km. Methodology included the selection of safety performance indicators on SP and MPU, use of nonparametric Wilcoxon signed rank test for safety performance indicator comparisons and Pearson Chi-Square to test the association between CP or NCP and low or high indicator values. Hypothesis test results point in the same direction and partially confirm the initial assumption that CP induces safer behavior in terms of speeding. The statistically sound results showed that CP drivers engaged in less speeding episodes and mobile phone use duration in comparison to NCP drivers, as well as lower speed while using a mobile phone. In addition, driver behavior in CP and NCP situations also differed in terms of the type of MPU, with the proportion of types of use that demand a higher level of visual and manual distraction being higher among NCP drivers. In summary, these results confirm the initial hypothesis of safer driving behavior during carpooling in terms of MPU while driving.

Driver Distraction Detection Methods: A Literature Review and Framework

Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. To reduce road accidents, the development of information systems to detect driver inattention and distraction is essential. Currently, distraction detection systems for road vehicles are not yet widely available or are limited to specific causes of driver inattention such as driver fatigue. Despite the increasing automation of driving due to the availability of increasingly sophisticated assistance systems, the human driver will continue to play a longer role as supervisor of vehicle automation. With this in mind, we review the published scientific literature on driver distraction detection methods and integrate the identified approaches into a holistic framework that is the main contribution of the paper. Based on published scientific work, our driver distraction detection framework contains a structured summary of reviewed approaches for detecting the three main distraction detection approaches: manual distraction, visual distraction, and cognitive distraction. Our framework visualizes the whole detection information chain from used sensors, measured data, computed data, computed events, inferred behavior, and inferred distraction type. Besides providing a sound summary for researchers interested in distracted driving, we discuss several practical implications for the development of driver distraction detection systems that can also combine different approaches for higher detection quality. We think our research can be useful despite - or even because of - the great developments in automated driving.

Three-Dimensional Spinal Position With and Without Manual Distraction Load Increases Spinal Height

ObjectiveThe purpose of this study was to investigate if spinal height increases using 3-dimensional (3-D) spinal position with and without manual distraction load and to assess the correlation between spine height changes and degrees of trunk rotation. MethodsFifty-six participants were randomly placed in one of two groups: (1) 3-D spinal position with manual distraction load, and (2) without manual distraction load. Spinal height was measured before and after the interventions using a stadiometer. For the statistical analysis, we used a 2 (Loading status: pre- versus post-intervention height) X 2 (3-D spinal position: with versus without manual distraction load) repeated measures Analysis of Variance (ANOVA) was used to identify significant interaction and main effects. Paired t-tests were used to calculate differences in spinal height changes between the two interventions. Pearson correlation coefficient was used to measure correlations between changes in spinal heights and degrees of trunk rotation. ResultsMean spinal height increase with 3-D spinal position with and without manual distraction load was 6.30 mm (±6.22) and 5.69 mm (±4.13), respectively. No significant interaction effect was present between loading status and 3-D spinal position but a significant main effect in loading status was. Paired t-tests revealed significant differences in spinal heights between pre-and post-3-D spinal position with and without manual distraction load. No significant correlation was measured between trunk rotation and spinal height changes. Conclusion3-D spinal position with or without distraction load increased spinal height. This suggests that 3-D spinal positioning without manual distraction could be used in home settings to help maintain intervertebral disc (IVD) health.

Detection of driver manual distraction via image-based hand and ear recognition

Driving distraction is a leading cause of fatal car accidents, and almost nine people are killed in the US each day because of distracting activities. Therefore, reducing the number of distraction-affected traffic accidents remains an imperative issue. A novel algorithm for detection of drivers’ manual distraction was proposed in this manuscript. The detection algorithm consists of two modules. The first module predicts the bounding boxes of the driver's right hand and right ear from RGB images. The second module takes the bounding boxes as input and predicts the type of distraction. 106,677 frames extracted from videos, which were collected from twenty participants in a driving simulator, were used for training (50%) and testing (50%). For distraction classification, the results indicated that the proposed framework could detect normal driving, using the touchscreen, and talking with a phone with F1-score 0.84, 0.69, 0.82, respectively. For overall distraction detection, it achieved F1-score of 0.74. The whole framework ran at 28 frames per second. The algorithm achieved comparable overall accuracy with similar research, and was more efficient than other methods. A demo video for the algorithm can be found at https://youtu.be/NKclK1bHRd4.

Video-Based Driver’s Hand Tracking using Fast Normalized Cross Coefficient with Improved Computational Efficiency

Driver distraction is one of the major causes for fatal car accidents. In a distracting activity, manual distraction is a triggered response of other types of distraction, such as cognitive and visual distraction. Therefore, recognition of manual distraction can contribute to the monitoring of overall drivers’ distraction. In this study, a computer vision-based method to track hand movement from the recorded driving behavior is proposed. This method integrates a low computational cost template matching algorithm using fast normalized cross coefficient (NCC) and a novel searching strategy. The proposed method was evaluated by the VIVA hand tracking data set. It achieves 50.83% of marginal accuracy percentage (mAP), 42.18% of multiple object tracking accuracy (MOTA), 31.56% of mostly tracked (MT), and 19.29% of mostly lost (ML), and it outperformed a state of the art algorithm in MOTA and MT. Additionally, the computational cost of the proposed method is greatly improved, and it can run at around 11.1 frames per second. The outcome of this research will further assist driving distraction recognition and mitigation, and improve driving safety.

Evaluation of a self-retaining distractor for hip joint arthroscopy in toy breed dogs

BackgroundHip arthroscopy has become a viable option over the last few years for small animal orthopedic diseases, including hip dysplasia, osteoarthritis, and joint evaluation. However, the narrow joint spaces make it difficult to manipulate the instrument, and depth of tissues make it difficult to distract the joint space. In addition, it is very difficult to maintain consistent distraction over time with a manual distraction due to hand fatigue. To overcome these difficulties, distractors are used in human medicine to improve safety and accuracy of arthroscopy. Therefore, in this study, distractor devices were applied to hip joints in small toy breed dogs to evaluate their technical efficacy. Potential iatrogenic neurovascular and articular damage were also evaluated by comparing two techniques for performing hip joint arthroscopy: the self-retaining distractor and external manipulation.ResultsThe mean ± SD of the joint distraction distance was 8.88 ± 3.54 mm in the self-retaining distraction group and 2.37 ± 0.82 mm in the manual traction group. As the joint space increased, surgeons could more easily place an arthroscopy portal and more comfortably manipulate the instrument with a distractor device. Furthermore, the acetabular cartilage damage (p = 0.004) was significantly greater in the external manipulation group, but articular damage to the femoral head (p = 0.940) was similar in both groups.ConclusionThe results of this study revealed that use of a distractor device can be a viable option for performing hip arthroscopy in small animals. The device significantly improved the surgeon’s performance without surgical assistance, and it reduced iatrogenic cartilage damage compared with manual traction. Further study is needed to quantify neurapraxia associated with distractor placement.

Road safety and distraction, results from a responsibility case-control study among a sample of road users interviewed at the emergency room

Despite the complexities of the driving task, more and more drivers engage in non-driving secondary tasks that take their hands (manual distraction), their eyes (visual distraction) and/or their mind (cognitive distraction) away from their primary task. Inattention arising from external distractions has received much less consideration beyond the impact of mobile phone use. We aimed to investigate the association between distraction behind the wheel and risk of being responsible for a road traffic crash in a responsibility case-control study. The study population included 1912 drivers injured in a road traffic crash recruited in two rounds of recruitment (from April 2010 to August 2011 and from March 2013 to January 2015) in the adult emergency department of Bordeaux University Hospital (France). Responsibility levels were estimated using a standardized method. Self-reported activities among a pre-established list of potential distractions were combined into four external distraction variables: visual distraction, manual distraction, auditory distraction, and verbal interaction. A significantly increased risk of being responsible for a road traffic crash was associated with the exposure to activities that take drivers’ eyes off the road (adjusted odds ratio 2.99, 95% confidence interval 1.42–6.28) and activities that take drivers’ hands off the wheel (adjusted odds ratio 2.12, 95% confidence interval 1.20–3.75). No significant associations were found for verbal interaction and listening to the radio and/or singing. This study suggests that beyond the use of mobile phone, particular attention must be paid to activities that involve visual and/or manual distraction.

Causes of peroneal neuropathy associated with orthopaedic leg lengthening in different canine models

Peroneal neuropathy is one of the complications of orthopaedic leg lengthening. Methods of treatment include slowing of distraction and decompression both of which may lead to additional complications. The purpose of this study was to analyse the changes in histologic peroneal nerve structure during experimental orthopaedic lengthening using various modes of manual or automatic distraction. The obtained data provide the basis for better understanding of peroneal neuropathy pathogenesis and refinement of prophylaxis and preventive treatment protocols. Four experimental models of canine leg lengthening using the Ilizarov fixator were studied: 1 (n = 10)—manual distraction—1 mm/day divided into four increments; 2 (n = 12)—automatic distraction—1 mm/day in 60 increments, 3 (n = 9) and 4 (n = 9)—increased rate of high frequency automatic distraction: 3 mm/day in 120 and 180 increments, respectively. In peroneal nerves semi-thin sections cross-sectional fascicular areas, content of adipocytes in epineurium, endoneurial vascularisation, morphometric parameters of nerve fibres were assessed by computerised analysis at the end of distraction and of consolidation periods and 30 days after fixator removal. In Groups 1–2 massive nerve fibre degeneration along with epineural vessels obliteration was revealed in two cases from 22, whereas in Groups 3–4 there were 10 from 18 (p < 0.01). Injuries of perineurium and endoneurial vessels were noted in Group 3, and long-lasting thinning of nerve fascicles in Group 4. The decrease in epineurial fat tissue was revealed in all groups, more drastic in 3. Modifications and injuries of nerve sheaths and blood vessels depending on distraction rate and frequency contribute to peroneal neuropathy. Its mechanical, circulatory and metabolic causes are discussed.

Three-Dimensional Analysis of Airway Change After LeFort III Midface Advancement With Distraction.

LeFort III midface advancement using the distraction technique became the standard method for the correction of various craniofacial syndromes accompanied by the midface retrusion. Midface hypoplasia is known to be related to the imbalance in facial aesthetics, malocclusion, and the airway problem. This study aimed to evaluate the change in airway volume after performing a LeFort III midface advancement with the distraction techniques. Between April 2008 and February 2013, 7 patients aged 5 to 7 years underwent standard LeFort III osteotomy, followed by distraction with a rigid external distractor with or without internal distractor. The degree of advancement of the midface and the airway volume were evaluated with 3-dimensional computed tomography scans and the 3-dimensional software. The average latent period was 3.57 days. The average manual distraction distance was 17.55 mm for 3.82 weeks. The consolidation period ranged from 3 to 7 months. Changes between the preoperative and postoperative airway volumes were remarkable. The average preoperative postpharyngeal airway volume was 5649.33 mm compared with an average postoperative airway volume of 7403.44 mm. Therefore, the average postpharyngeal airway space increased by a remarkable 32.78%. This investigation revealed that the LeFort III midface advancement using distraction could increase postpharyngeal airway space by approximately 33% when the midface is advanced by approximately 18 mm. This method could be used as a future reference for LeFort III midface advancement with distraction.

Do Hands-Free Cell Phone Conversation While Driving Increase the Risk of Cognitive Distraction Among Drivers? A Cross-Over Quasi-Experimental Study

Background: Background: Conversation over the cell phone while driving is a known risk factor for road traffic crash. Using handsfree to talk on the cell phone may remove visual and manual distraction yet not the cognitive distraction. Objectives: The purpose of this study was to better understand the mechanisms of cognitive distraction due to hands-free cell phone conversation (HFPC) while driving. Methods: Twelve male and 12 female University students in Tehran, Iran, were selected via the consecutive convenient sampling method, and randomly assigned to one of the following administrations of cross-over quasi-experimental study design, during year 2016: Administration 1, participants performed the tests while involved in HFPC, took rest for 60 minute, and then performed the tests another time without HFPC; Administration 2, participants performed the tests without HFPC, took rest, and then performed the tests another time with HFPC. Each participant moved to the other administration after 7 to 10 days. The Vienna test system (VTS) was used to measure simple and choice reaction time, selective attention, visual orientation, and visual memory. Linear regression analysis was used to study the change in test scores due to HFPC. Results: The mean age of participants was 27.1±5.3 years. A history of road traffic crash (regardless of severity) was reported among 9 (37.5) participants in the previous year. Hands-free cell phone conversation while driving was directly associated with mean time correct rejection score (P < 0.01) (selective attention), omitted response (P < 0.01) and median reaction time (P < 0.01) (choice reaction time), and mean reaction time (P < 0.01) (Simple reaction time). Moreover, HPFC was inversely associated with sum hits (P = 0.05) (selective attention), incorrect (P < 0.02) and correct response (P < 0.01) (choice reaction time), score based on viewing and working time (P < 0. 01) (visual orientation), and visual memory performance (P < 0.01). Conclusions: Using hands-free devices to converse during driving, impaired reaction time, selective attention, visual orientation and visual memory, which are essential for safe driving. Thus, the use of these devices does not preclude cognitive distraction and should be restricted. © 2018, Iranian Red Crescent Medical Journal.

Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving

Given the proliferation of ‘intelligent’ and ‘socially-aware’ digital assistants embodying everyday mobile technology – and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices – it appears inevitable that next generation vehicles will be embodied by digital assistants and utilise spoken language as a method of interaction. From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis – formulating responses; turn-taking; back-channelling, fillers and hesitation; vague language; mitigating requests and politeness and praise. The results can be used to inform the design of future in-vehicle natural language systems, in particular to help manage the tension between designing for an engaging dialogue (important for technology acceptance) and designing for an effective dialogue (important to minimise distraction in a driving context).

Benefits of Ilizarov automated bone distraction for nerves and articular cartilage in experimental leg lengthening.

AIMTo determine peculiarities of tissue responses to manual and automated Ilizarov bone distraction in nerves and articular cartilage.METHODSTwenty-nine dogs were divided in two experimental groups: Group M - leg lengthening with manual distraction (1 mm/d in 4 steps), Group A - automated distraction (1 mm/d in 60 steps) and intact group. Animals were euthanized at the end of distraction, at 30th day of fixation in apparatus and 30 d after the fixator removal. M-responses in gastrocnemius and tibialis anterior muscles were recorded, numerical histology of peroneal and tibialis nerves and knee cartilage semi-thin sections, scanning electron microscopy and X-ray electron probe microanalysis were performed.RESULTSBetter restoration of M-response amplitudes in leg muscles was noted in A-group. Fibrosis of epineurium with adipocytes loss in peroneal nerve, subperineurial edema and fibrosis of endoneurium in some fascicles of both nerves were noted only in M-group, shares of nerve fibers with atrophic and degenerative changes were bigger in M-group than in A-group. At the end of experiment morphometric parameters of nerve fibers in peroneal nerve were comparable with intact nerve only in A-group. Quantitative parameters of articular cartilage (thickness, volumetric densities of chondrocytes, percentages of isogenic clusters and empty cellular lacunas, contents of sulfur and calcium) were badly changed in M-group and less changed in A-group.CONCLUSIONAutomated Ilizarov distraction is more safe method of orthopedic leg lengthening than manual distraction in points of nervous fibers survival and articular cartilage arthrotic changes.

Individual glance strategies and their effect on the NHTSA visual manual distraction test

The purpose of this paper was to investigate how individual differences in glance strategy could impact the glance performance test defined in the NHTSA visual manual distraction guidelines. Better understanding of the test procedure could help development of new technology for safe driving. A custom in-vehicle information system was developed and assessed in a driving simulator by eighteen participants. The interfaces were designed according to recommendations in the NHTSA guidelines and contained manual radio-tuning tasks, sound settings tasks and six letter spelling tasks. Two of the six tested interfaces fully complied with the test. In addition, clear individual differences in glance strategy were found among the participants. Four individual glance strategies were identified. Two of these, long glancers and frequent glancers, highly affected the outcome of the compliance test. Participants belonging to the long glancers and the frequent glancers categories were identified as statistical outliers in many test cases. For example, if the individual values of these participants were replaced with sample mean, the number of complying interfaces would increase to five out of six, which is more in line with expectations for these interfaces. The results of this study show that individual variations in glance strategy exist. Also, these individual variations seem to have a non- negligible influence on the result when performance-testing of in-vehicle interfaces is done according to the NTHSA guidelines.