Yoichi Tomioka

Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa1 et al.

The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent suicide attempts resulting from medication overdose, underscores the critical importance of accurate and efficient fall detection methods. In this scenario, a computer-aided fall detection system is inevitable to save elderly people's lives worldwide. Many researchers have been working to develop fall detection systems. However, the existing fall detection systems often struggle with issues such as unsatisfactory performance accuracy, limited robustness, high computational complexity, and sensitivity to environmental factors due to a lack of effective features. In response to these challenges, this paper proposes a novel three-stream spatial-temporal feature-based fall detection system. Our system incorporates joint skeleton-based spatial and temporal Graph Convolutional Network (GCN) features, joint motion-based spatial and temporal GCN features, and residual connections-based features. Each stream employs adaptive graph-based feature aggregation and consecutive separable convolutional neural networks (Sep-TCN), significantly reducing computational complexity and model parameters compared to prior systems. Experimental results across multiple datasets demonstrate the superior effectiveness and efficiency of our proposed system, with accuracies of 99.51\%, 99.15\%, 99.79\% and 99.85 \% achieved on the ImViA, UR-Fall, Fall-UP and FU-Kinect datasets, respectively. The remarkable performance of our system highlights its superiority, efficiency, and generalizability in real-world fall detection scenarios, offering significant advancements in healthcare and societal well-being.

Kai Su, Yoichi Tomioka, Qiangfu Zhao et al.

In the realm of Tiny AI, we introduce ``You Only Look at Interested Cells" (YOLIC), an efficient method for object localization and classification on edge devices. Through seamlessly blending the strengths of semantic segmentation and object detection, YOLIC offers superior computational efficiency and precision. By adopting Cells of Interest for classification instead of individual pixels, YOLIC encapsulates relevant information, reduces computational load, and enables rough object shape inference. Importantly, the need for bounding box regression is obviated, as YOLIC capitalizes on the predetermined cell configuration that provides information about potential object location, size, and shape. To tackle the issue of single-label classification limitations, a multi-label classification approach is applied to each cell for effectively recognizing overlapping or closely situated objects. This paper presents extensive experiments on multiple datasets to demonstrate that YOLIC achieves detection performance comparable to the state-of-the-art YOLO algorithms while surpassing in speed, exceeding 30fps on a Raspberry Pi 4B CPU. All resources related to this study, including datasets, cell designer, image annotation tool, and source code, have been made publicly available on our project website at https://kai3316.github.io/yolic.github.io

Stanislav Sedukhin, Yoichi Tomioka, Kazuya Matsumoto et al.

Multilinear transformations are key in high-performance computing (HPC) and artificial intelligence (AI) workloads, where data is represented as tensors. However, their high computational and memory demands, which grow with dimensionality, often slow down critical tasks. Moreover, scaling computation by enlarging the number of parallel processing units substantially increases energy consumption, limiting widespread adoption, especially for sparse data, which is common in HPC and AI applications. This paper introduces the Trilinear Algorithm and isomorphic to algorithm Device Architecture (TriADA) to address these challenges with the following innovations: (1) a massively parallel, low-rank algorithm for computing a family of trilinear (3D) discrete orthogonal transformations (3D-DXTs), which is a special case of the more general 3-mode matrix-by-tensor multiplication (3D-GEMT); (2) a new outer-product-based GEMM kernel with decoupled streaming active memory, specially designed to accelerate 3D-GEMT operation; (3) an isomorphic to the proposed algorithm, fully distributed 3D network of mesh interconnected processing elements or cells with a coordinate-free, data-driven local processing activity, which is independent of problem size; (4) an elastic sparse outer-product (ESOP) method that avoids unnecessary computing and communication operations with zero-valued operands, thereby enhancing energy efficiency, computational accuracy, and stability. TriADA is capable of performing a variety of trilinear transformations with hypercubic arithmetic complexity in a linear number of time-steps. The massively parallel, scalable, and energy-efficient architecture of TriADA is ideal for accelerating multilinear tensor operations, which are the most demanding parts of AI and HPC workloads.