Body condition scoring (BCS) is a farm-management tool for estimating dairy cows’ energy reserves. Today, BCS is performed manually by experts. This paper presents a 3-dimensional algorithm that ...provides a topographical understanding of the cow’s body to estimate BCS. An automatic BCS system consisting of a Kinect camera (Microsoft Corp., Redmond, WA) triggered by a passive infrared motion detector was designed and implemented. Image processing and regression algorithms were developed and included the following steps: (1) image restoration, the removal of noise; (2) object recognition and separation, identification and separation of the cows; (3) movie and image selection, selection of movies and frames that include the relevant data; (4) image rotation, alignment of the cow parallel to the x-axis; and (5) image cropping and normalization, removal of irrelevant data, setting the image size to 150×200 pixels, and normalizing image values. All steps were performed automatically, including image selection and classification. Fourteen individual features per cow, derived from the cows’ topography, were automatically extracted from the movies and from the farm’s herd-management records. These features appear to be measurable in a commercial farm. Manual BCS was performed by a trained expert and compared with the output of the training set. A regression model was developed, correlating the features with the manual BCS references. Data were acquired for 4 d, resulting in a database of 422 movies of 101 cows. Movies containing cows’ back ends were automatically selected (389 movies). The data were divided into a training set of 81 cows and a test set of 20 cows; both sets included the identical full range of BCS classes. Accuracy tests gave a mean absolute error of 0.26, median absolute error of 0.19, and coefficient of determination of 0.75, with 100% correct classification within 1 step and 91% correct classification within a half step for BCS classes. Results indicated good repeatability, with all standard deviations under 0.33. The algorithm is independent of the background and requires 10 cows for training with approximately 30 movies of 4 s each.
Objective
Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators.
Background
...Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators’ cognitive workloads.
Method
Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models.
Results
Lower cognitive workload was found when applying the algorithm’s protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose.
Conclusion
The algorithm was positively evaluated for its ability to reduce operators’ cognitive workloads. More evaluations of operators’ SA are required.
Application
The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.
•Cell phone conversations jeopardize pedestrians’ ability to safely cross the road.•Cell phone conversations affect children and adults’ crossing abilities similarly.•Pedestrians’ visual attention ...distribution changes when busy with a phone conversation.•The ability to cross the road safely is age related.•Children aged 11–13 showed safe crossing performance, yet differ from adults.
Child pedestrians are highly represented in fatal and severe road crashes and differ in their crossing behavior from adults. Although many children carry cell phones, the effect that cell phone conversations have on children’s crossing behavior has not been thoroughly examined. A comparison of children and adult pedestrians’ crossing behavior while engaged in cell phone conversations was conducted. In a semi-immersive virtual environment simulating a typical city, 14 adults and 38 children (11 children aged 7–8; 18 aged 9–10 and 9 aged 11–13), experienced road crossing related traffic-scene scenarios. They were requested to press a response button whenever they felt it was safe to cross. Eye movements were tracked. Results have shown that all age groups’ crossing behaviors were affected by cell phone conversations. When busy with more cognitively demanding conversation types, participants were slower to react to a crossing opportunity, chose smaller crossing gaps, and allocated less visual attention to the peripheral regions of the scene. The ability to make better crossing decisions improved with age, but no interaction with cell phone conversation type was found. The most prominent improvement was shown in ‘safety gap’; each age group maintained a longer gap than its predecessor younger age group. In accordance to the current study, it is safe to say that cell phone conversations can hinder child and adult pedestrians’ safety. Thereby, it is important to take those findings in account when aiming to train young pedestrians for road-safety and increase public awareness.
Advanced age and brain damage have been reported to increase the propensity to gaze down while walking, a behavior that is thought to enhance stability through anticipatory stepping control. ...Recently, downward gazing (DWG) has been shown to enhance postural steadiness in healthy adults, suggesting that it can also support stability through a feedback control mechanism. These results have been speculated to be the consequence of the altered visual flow when gazing down. The main objective of this cross-sectional, exploratory study was to investigate whether DWG also enhances postural control in older adults and stroke survivors, and whether such effect is altered with aging and brain damage.
Posturography of older adults and stroke survivors, performing a total of 500 trials, was tested under varying gaze conditions and compared with a cohort of healthy young adults (375 trials). To test the involvement of the visual system we performed spectral analysis and compared the changes in the relative power between gaze conditions.
Reduction in postural sway was observed when participants gazed down 1 and 3 meters ahead whereas DWG towards the toes decreased steadiness. These effects were unmodulated by age but were modulated by stroke. The relative power in the spectral band associated with visual feedback was significantly reduced when visual input was unavailable (eyes-closed condition) but was unaffected by the different DWG conditions.
Like young adults, older adults and stroke survivors better control their postural sway when gazing down a few steps ahead, but extreme DWG can impair this ability, especially in people with stroke.
In this study, we contributed to closing this gap by providing a framework for identifying patients with inflammatory bowel disease (IBD) on Twitter and learning from their personal experiences. We ...enabled the analysis of patients’ tweets by building a classifier of Twitter users that distinguishes patients from other entities. This study aimed to uncover the potential of using Twitter data to promote the well-being of patients with IBD by relying on the wisdom of the crowd to identify healthy lifestyles. We sought to leverage posts describing patients’ daily activities and their influence on their well-being to characterize lifestyle-related treatments. In the first stage of the study, a machine learning method combining social network analysis and natural language processing was used to automatically classify users as patients or not. We considered 3 types of features: the user’s behavior on Twitter, the content of the user’s tweets, and the social structure of the user’s network. We compared the performances of several classification algorithms within 2 classification approaches. One classified each tweet and deduced the user’s class from their tweet-level classification. The other aggregated tweet-level features to user-level features and classified the users themselves. Different classification algorithms were examined and compared using 4 measures: precision, recall, F1 score, and the area under the receiver operating characteristic curve. In the second stage, a classifier from the first stage was used to collect patients' tweets describing the different lifestyles patients adopt to deal with their disease. Using IBM Watson Service for entity sentiment analysis, we calculated the average sentiment of 420 lifestyle-related words that patients with IBD use when describing their daily routine. Both classification approaches showed promising results. Although the precision rates were slightly higher for the tweet-level approach, the recall and area under the receiver operating characteristic curve of the user-level approach were significantly better. Sentiment analysis of tweets written by patients with IBD identified frequently mentioned lifestyles and their influence on patients’ well-being. The findings reinforced what is known about suitable nutrition for IBD as several foods known to cause inflammation were pointed out in negative sentiment, whereas relaxing activities and anti-inflammatory foods surfaced in a positive context. This study suggests a pipeline for identifying patients with IBD on Twitter and collecting their tweets to analyze the experimental knowledge they share. These methods can be adapted to other diseases and enhance medical research on chronic conditions.
Downward gazing is often observed when walking requires guidance. This gaze behavior is thought to promote walking stability through anticipatory stepping control. This study is part of an ongoing ...effort to investigate whether downward gazing also serves to enhance postural control, which can promote walking stability through a feedback/reactive mechanism. Since gaze behavior alone gives no indication as to what information is gathered and the functions it serves, we aimed to investigate the cognitive demands associated with downward gazing, as they are likely to differ between anticipatory and feedback use of visual input. To do so, we used a novel methodology to compromise walking stability in a manner that could not be resolved through modulation of stepping. Then, using interference methodology and neuroimaging, we tested for (1) interference related to dual tasking, and (2) changes in prefrontal activity. The novel methodology resulted in an increase in the time spent looking at the walking surface. Further, while some dual-task interference was observed, indicating that this gaze behavior is cognitively demanding, several gaze parameters pertaining to downward gazing and prefrontal activity correlated. These correlations revealed that a greater tendency to gaze onto the walking surface was associated with lower PFC activity, as is expected when sensory information is used through highly automatic, and useful, neural circuitry. These results, while not conclusive, do suggest that gazing onto the walking surface can be used for purposes other than anticipatory stepping control, bearing important motor-control and clinical implications.
This study explored pedestrians' understanding of Fully Autonomous Vehicles (FAVs) intention to stop and what influences pedestrians' decision to cross the road over time, i.e., learnability. Twenty ...participants saw fixed simulated urban road crossing scenes with a single FAV on the road as if they were pedestrians intending to cross. Scenes differed from one another in the FAV's, distance from the crossing place, its physical size, and external Human-Machine Interfaces (e-HMI) message by background color (red/green), message type (status/advice), and presentation modality (text/symbol). Eye-tracking data and decision measurements were collected. Results revealed that pedestrians tend to look at the e-HMI before making their decision. However, they did not necessarily decide according to the e-HMIs' color or message type. Moreover, when they complied with the e-HMI proposition, they tended to hesitate before making the decision. Overall, a learning effect over time was observed in all conditions regardless of e- HMI features and crossing context. Findings suggest that pedestrians' decision making depends on a combination of the e-HMI implementation and the car distance. Moreover, since the learning curve exists in all conditions and has the same proportion, it is critical to design an interaction that would encourage higher probability of compatible decisions from the first phase. However, to extend all these findings, it is necessary to further examine dynamic situations.
Background. The extent to which the upper-limb flexor synergy constrains or compensates for arm motor impairment during reaching is controversial. This synergy can be quantified with a minimal marker ...set describing movements of the arm-plane. Objectives. To determine whether and how (a) upper-limb flexor synergy in patients with chronic stroke contributes to reaching movements to different arm workspace locations and (b) reaching deficits can be characterized by arm-plane motion. Methods. Sixteen post-stroke and 8 healthy control subjects made unrestrained reaching movements to targets located in ipsilateral, central, and contralateral arm workspaces. Arm-plane, arm, and trunk motion, and their temporal and spatial linkages were analyzed. Results. Individuals with moderate/severe stroke used greater arm-plane movement and compensatory trunk movement compared to those with mild stroke and control subjects. Arm-plane and trunk movements were more temporally coupled in stroke compared with controls. Reaching accuracy was related to different segment and joint combinations for each target and group: arm-plane movement in controls and mild stroke subjects, and trunk and elbow movements in moderate/severe stroke subjects. Arm-plane movement increased with time since stroke and when combined with trunk rotation, discriminated between different subject groups for reaching the central and contralateral targets. Trunk movement and arm-plane angle during target reaches predicted the subject group. Conclusions. The upper-limb flexor synergy was used adaptively for reaching accuracy by patients with mild, but not moderate/severe stroke. The flexor synergy, as parameterized by the amount of arm-plane motion, can be used by clinicians to identify levels of motor recovery in patients with stroke.
The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a ...commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.
Nonconformity (NC) management is a fundamental process in production, yet the literature notion of it does not always align with what is practiced in reality. In particular, the literature often ...excludes the NC responsibility decision, which is a difficult, costly and time-consuming task assignment, but also an integral part of the NC management process. We propose a semi-automated model we call SANC, which improves the accuracy of NC responsibility decisions and significantly cuts their costs. We base our methodology on CRISP-DM and extend it to fit the semi-automated NC responsibility decision. Unlike the original CRISP-DM, SANC utilizes existing organizational resources, and thus extends the capabilities of CRISP-DM in terms of both achieving greater overall performance and broadening its appeal to more traditional production processes. We demonstrate this solution by implementing it in a large-scale assembly plant in the printing industry, that may result in savings of over $186 K according to our assessments.
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