Materials and Method: Data were collected from 84 field hockey players that participated in the Malaysian Hockey League competition from June 2016 until December 2016. All injuries were recorded by the participating medical team using a structured questionnaire. A descriptive statistical analysis and Chi-Square test were used to explore the prevalence of the injury.

Result: More than half of the players were reported to have lower limb injuries (51.6%). Sprain and strain were the most prevalent injuries (63%) and mostly affected the ankle (29%). Male players sustained more injuries (50.8%) compared to female players (49.2%).

OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.

RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.

Methods: Eighteen male subjects ran on three different surfaces (i.e., concrete, artificial grass, and rubber) in both heeled running shoes (HS) and minimal running shoes (MS). Both these shoes had dissimilar sole profiles. The heeled shoes had a higher sole at the heel, a thick base, and arch support, whereas the minimal shoes had a flat base sole. Indeed, the studied biomechanical parameters responded differently in the different footwear during running. Subjects ran in recreational mode speed while 3D foot kinematics (i.e., joint rotation and peak medial longitudinal arch (MLA) angle) were determined using a motion capture system (Qualysis, Gothenburg, Sweden). Information on stance time and plantar fascia strain (PFS) was also collected.

Results: Running on different surface stiffness was found to significantly affect the peak MLA angles and stance times for both HS and MS conditions. However, the results showed that the joint rotation angles were not sensitive to surface stiffness. Also, PFS showed no relationship with surface stiffness, as the results were varied as the surface stiffness was changed.