Claudication is the most common symptomatic manifestation of peripheral arterial disease (PAD), producing significant ambulatory compromise. The purpose of this study was to use advanced biomechanical gait analysis to determine the gait alterations occurring in claudicating patients both before and after onset of claudication pain in their legs. Hip, knee, and ankle joint moments were measured in claudicating patients (age: 64.4678.47 years; body mass: 80.70712.64 kg; body height: 1.7270.08 m) and were compared to gender-age-body mass-height-matched healthy controls (age 66.2779.22 years; body mass: 77.89710.65 kg; body height: 1.7470.08 m). The claudicating patients were evaluated both before (pain-free (PF) condition) and after (pain condition) onset of claudication pain in their legs. Thirteen symptomatic PAD patients (26 claudicating limbs) with bilateral intermittent claudication (IC) and 11 healthy controls (22 control limbs) were tested during level walking at their self-selected speed. Compared to controls, PAD hip and ankle joints demonstrated significant angular kinematics and net internal moment changes. Alterations were present both in PF and pain conditions with several of them becoming worse in the pain condition. Both PF and pain conditions resulted in significantly reduced peak hip extensor moment (5.6271.40 and 5.6371.33% BW Â BH, respectively) during early stance as compared to controls (7.5371.16% BW Â BH). In the pain condition, PAD patients had a significantly reduced ankle plantar flexor moment (7.5671.41% BW Â BH) during late stance as compared to controls (8.6571.27% BW Â BH). Furthermore, when comparing PF to pain conditions, there was a decreased peak plantar flexor moment (PF condition: 8.2371.37 vs. pain condition: 7.5671.41% BW Â BH) during late stance. The findings point to a weakness in the posterior compartment muscles of the hip and calf as being the key factor underlying the PAD gait adaptations.Our findings establish a detailed baseline description of the changes present in PAD patient's joint angles and moments during walking. Since IC is primarily a gait disability, better understanding of the abnormalities in joint and muscle function will enhance our understanding of the gait impairment and may lead to novel, gait-specific treatments. r

Abstract:There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network's training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the "YOLO" ("You Only Look Once") platform, objects can be tracked, detected ("seen"), and classified ("comprehended") from video feeds supplied by UAVs in real-time.

No abstract

No abstract

No abstract