Keynote Speech: Technologies for Smart Transportation

The application of ITS is in an infant stage in India. The traffic stream in the western countries are lane based in nature with the major traffic composition including cars and a fewer percentage of trucks, which makes the data collection from the detectors less challenging. However, the Indian traffic being composed of different varieties of vehicles such as two-wheelers, three-wheelers, cars, buses and trucks moving without any lane disciplines makes the data collection a challenging task. Identification of suitable sensors for data collection under Indian traffic conditions by itself is a challenge. Numerous researches are currently being carried out to analyse the effectiveness of sensors for data collection under Indian traffic conditions such as Bluetooth sensors, Wi-Fi sensors, RFID sensors etc.

Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.