Monitoring the number of jumps for volleyball players during training or a match can be crucial to prevent injuries, yet the measurement requires considerable workload and cost using traditional methods such as video analysis. Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sample-wise classification. The model was evaluated on ten volleyball players and twenty-six volleyball players, during a lab session with a fixed protocol of jumping and landing tasks, and during four volleyball training sessions, respectively. The MS-TCN model achieved better performance than a state-of-the-art deep learning model but with lower computational cost. In the lab sessions, most jump counts showed small differences between the predicted jumps and video-annotated jumps, with an overall count showing a Limit of Agreement (LoA) of 0.1+-3.40 (r=0.884). For comparison, the proposed algorithm showed slightly worse results than VERT (a commercial jumping assessment device) with a LoA of 0.1+-2.08 (r=0.955) but the differences were still within a comparable range. In the training sessions, the recognition of three types of jumps exhibited a mean difference from observation of less than 10 jumps: block, smash, and overhead serve. These results showed the potential of using a single IMU to recognize the types of volleyball jumps. The sample-wise architecture provided high resolution of recognition and the MS-TCN required fewer parameters to train compared with state-of-the-art models.

"Atos has developed a differentiated experience with its North American AI Lab to provide customers tangible results which they can use to kick-start their AI strategy and take into the field immediately," said Peter Cutts, Chief Digital Transformation Officer, Atos North America. "Customers are looking for industry-specific solutions for their business needs, not a cookie cutter approach. The Atos AI Lab approach empathizes with end users' needs and engages multiple stakeholders to deliver real-world code, datasets and solutions that are repeatable and globally scalable." The Atos AI Lab is a state-of-the-art facility that combines a digital experience with design thinking methodology to allow participants to problem solve and create in a format that works best for them. The Atos AI Lab offers an Incubation workshop that aims to create a use-case ready to deploy at the end of two days, meaning customers can start driving business results quickly.

"Atos has developed a differentiated experience with its North American AI Lab to provide customers tangible results which they can use to kick-start their AI strategy and take into the field immediately," said Peter Cutts, Chief Digital Transformation Officer, Atos North America."Customers The Atos AI Lab approach empathizes with end users' needs and engages multiple stakeholders to deliver real-world code, datasets and solutions that are repeatable and globally scalable." The Atos AI Lab is a state-of-the-art facility that combines a digital experience with design thinking methodology to allow participants to problem solve and create in a format that works best for them. The Atos AI Lab offers an Incubation workshop that aims to create a use-case ready to deploy at the end of two days, meaning customers can start driving business results quickly. To achieve this, real-world customer data is coupled with an Atos-specific methodology to allow the customer to understand the business problem and leave the Atos AI Lab with a clear path on how to solve their challenges using big data and artificial intelligence tools.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.