A surgical instrument motion measurement system for skill evaluation in practical laparoscopic surgery training.

This study developed and validated a surgical instrument motion measurement system for skill evaluation during practical laparoscopic surgery training. Owing to the various advantages of laparoscopic surgery including minimal invasiveness, this technique has been widely used. However, expert surgeons have insufficient time for providing training to beginners due to the shortage of surgeons and limited working hours. Skill transfer efficiency has to be improved for which there is an urgent need to develop objective surgical skill evaluation methods. Therefore, a simple motion capture-based surgical instrument motion measurement system that could be easily installed in an operating room for skill assessment during practical surgical training was developed. The tip positions and orientations of the instruments were calculated based on the marker positions attached to the root of the instrument. Because the patterns of these markers are individual, this system can track multiple instruments simultaneously and detect exchanges. However due to the many obstacles in the operating room, the measurement data included noise and outliers. In this study, the effect of this decrease in measurement accuracy on feature calculation was determined. Accuracy verification experiments were conducted during wet-lab training to demonstrate the capability of this system to measure the motion of surgical instruments with practical accuracy. A surgical training experiment on a cadaver was conducted, and the motions of six surgical instruments were measured in 36 cases of laparoscopic radical nephrectomy. Outlier removal and smoothing methods were also developed and applied to remove the noise and outliers in the obtained data. The questionnaire survey conducted during the experiment confirmed that the measurement system did not interfere with the surgical operation. Thus, the proposed system was capable of making reliable measurements with minimal impact on surgery. The system will facilitate surgical education by enabling the evaluation of skill transfer of surgical skills.

Translational medical bioengineering research of traumatic brain injury among Chinese and American pedestrians caused by vehicle collision based on human body finite element modeling.

Based on the average human body size in China and the THUMS AM50 finite element model of the human body, the Kriging interpolation algorithm was used to model the Chinese 50th percentile human body, and the biological fidelity of the model was verified. We built three different types of passenger vehicle models, namely, sedan, sports utility vehicle (SUV), and multi-purpose vehicle (MPV), and used mechanical response analysis and finite element simulation to compare and analyze the dynamic differences and head injury differences between the Chinese 50th percentile human body and the THUMS AM50 model during passenger vehicle collisions. The results showed that there are obvious differences between the Chinese mannequin and THUMS in terms of collision time, collision position, invasion speed, and angle. When a sedan collided with the mannequins, the skull damage to the Chinese human body model was more severe, and when a sedan or SUV collided, the brain damage to the Chinese human body was more severe. The abovementioned results suggest that the existing C-NCAP pedestrian protection testing regulations may not provide the best protection for Chinese human bodies, and that the regulations need to be improved by combining collision damage mechanisms and the physical characteristics of Chinese pedestrians. This thorough investigation is positioned to shed light on the fundamental biomechanics and injury mechanisms at play. Furthermore, the amalgamation of clinically rooted translational and engineering research in the realm of traumatic brain injury has the potential to establish a solid foundation for discerning preventive methodologies. Ultimately, this endeavor holds the potential to introduce effective strategies aimed at preventing and safeguarding against traumatic brain injuries.

Translation and Cross-Cultural Adaptation of the Family Accommodation Scale for Obsessive-Compulsive Disorder Into Chinese.

Family accommodation is a phenomenon that has been associated with worse treatment outcome of patients with obsessive-compulsive disorder (OCD) and greater severity of symptoms and levels of functional impairment. Yet, there are no Chinese scales to assess family accommodation in OCD among family members. The present study aimed to illustrate the steps of translation and cross-cultural adaptation of the Chinese versions of the Family Accommodation Scale (FAS). After obtaining authorization of the developers, the Chinese versions of the FAS were translated and adapted from the English versions based on a standard protocol, following six steps: forward translation, pilot administration, language adjustment and cultural adaptation, back-translation, review and minor edit, and final approval of the developer. Thirty-five pairs of patients and corresponding relatives with different education levels were administered the FAS in the pretest stage. This study found that the semantic, idiomatic, and conceptual equivalence were obtained between the Chinese versions and original English scales, and the Chinese versions of FAS were well translated and culturally adapted. We also found that the Chinese versions of the FAS can be easily understood by people of different socioeconomic statuses.

Wrinkle-Enabled Highly Stretchable Strain Sensors for Wide-Range Health Monitoring with a Big Data Cloud Platform.

Flexible and stretchable strain sensors are vital for emerging fields of wearable and personal electronics, but it is a huge challenge for them to possess both wide-range measurement capability and good sensitivity. In this study, a highly stretchable strain sensor with a wide strain range and a good sensitivity is fabricated based on smart composites of carbon black (CB)/wrinkled Ecoflex. The sensor exhibits a maximum recoverable strain of up to 500% and a high gauge factor of 67.7. It has a low hysteresis, a fast signal response (as short as 120 ms), and a high reproducibility (up to 5000 cycles with a strain of 150%). The sensor is capable of detecting and capturing wide-range human activities, from speech recognition and pulse monitoring to vigorous motions. It is also applicable for real-time monitoring of robot movements and vehicle security crash in an anthropomorphic field. More importantly, the sensor is successfully used to send signals of a volunteer's breathing data to a local hospital in real time through a big data cloud platform. This research provides the feasibility of using a strain sensor for wearable Internet of things and demonstrates its exciting prospect for healthcare applications.

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning.

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

Could an isolated human body lower limb model predict leg biomechanical response of Chinese pedestrians in vehicle collisions?

PURPOSE: The purpose of the current study was to investigate whether an isolated human body lower limb FE model could predict leg kinematics and biomechanical response of a full body Chinese pedestrian model in vehicle collisions. METHODS: A human body lower limb FE model representing midsize Chinese adult male anthropometry was employed with different upper body weight attachments being evaluated by comparing the predictions to those of a full body pedestrian model in vehicle-to-pedestrian collisions considering different front-end shapes. RESULTS: The results indicate that upper body mass has a significant influence on pedestrian lower limb injury risk, the effect varies from vehicle front-end shape and is more remarkable to the femur and knee ligaments than to the tibia. In particular, the upper body mass can generally increase femur and knee ligaments injury risk, but has no obvious effect on the injury risk of tibia. The results also show that a higher attached buttock mass is needed for isolated pedestrian lower limb model for impacts with vehicles of higher bonnet leading edge. CONCLUSIONS: The findings of this study may suggest that it is necessary to consider vehicle shape variation in assessment of vehicle pedestrian protection performance and leg-form impactors with adaptive upper body mass should be used for vehicles with different front-end shapes, and the use of regional leg-form impactor modeling the local anthropometry to evaluate the actual lower limb injury of pedestrians in different countries and regions.

Comparison of current ATDs with Chinese adults in anthropometry.

OBJECTIVE: Crash test dummies are full-scale anthropomorphic test devices (ATDs) that simulate the dimensions, weight proportions, and articulation of the human body and are used to measure human injury potential in vehicle crashes. The Hybrid III dummy family, which is widely used currently, takes selected percentiles of anthropometry dimensions of U.S. adults as design references. The objective of this study was to assess the difference in anthropometry between Chinese adults and the currently used dummy. METHODS: Based on the Chinese National Physical Fitness Surveillance of the year 2000, 2005, 2010 and National Standard of China GB/T 10000-1988, a series of anthropometric parameters for Chinese adults were obtained, and data analysis was conducted between Chinese adults and ATDs that are currently used. RESULTS: The comparison revealed distinct anthropometric difference between ATDs and Chinese adults. Based on the latest data, median Chinese females were about 2.6% lower in stature and about 8.03% lower in body weight than the ATD design targets. Similarly, median Chinese males were about 3.48% shorter and weighed 11.89% less than the ATD design targets. CONCLUSIONS: Although the anthropometric differences between Chinese adults and the Hybrid III ATD specifications were modest and growing smaller, it is advisable to take the differences in anthropometry between ATDs and Chinese adults into consideration when developing new vehicles in China to provide effective protection specifically for Chinese occupants.