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Ergonomic Risk Prediction for Awkward Postures From 3D Keypoints Using Deep Learning
No full textWork-related musculoskeletal ailments are injuries or disorders of the joints, muscles, nerves, or tendons caused by repetitive tasks and jobs that require uncomfortable postures. REBA (Rapid Entire Body Assessment) is a widely used assessment method for examining occupational ergonomics in areas where musculoskeletal disorders (MSDs) are common. REBA assessment necessitates the presence of a professional evaluator who monitors workers’ motions and postures, which takes time and has limitations in terms of real-world implementation. With the progress of deep learning-based human posture estimate algorithms, postural risk assessment has become an important and complex research area. We present a technique for forecasting REBA risk levels using 3D coordinates of human body position as input data in this study. We calculated REBA risk scores for various body segments and overall risk rating for corresponding action level for each body position using 3D keypoints from the widely renowned Human 3.6M dataset, which is a significant contribution for future research work in this arena. Using this vast ground truth dataset, a unique DNN model was created to forecast the REBA risk level for measuring the full body’s postural risk. REBA Ground Truth dataset is highly imbalanced which coped with data augmentation for the rare classes. To determine the optimal model configuration based on highest accuracy, ablation study is conducted by tuning different hyper-parameters. The proposed model, post-ablation study, attained 89.07% accuracy score on a test set of 128,046 samples from Nadam optimizer with a learning rate of 0.001 and batch size of 512.journal articl
Automatic Thoughts and Facial Expressions in Cognitive Restructuring With Virtual Agents
No full textCognitive restructuring is a well-established mental health technique for amending automatic thoughts, which are distorted and biased beliefs about a situation, into objective and balanced thoughts. Since virtual agents can be used anytime and anywhere, they are expected to perform cognitive restructuring without being influenced by medical infrastructure or patients' stigma toward mental illness. Unfortunately, since the quantitative analysis of human-agent interaction is still insufficient, the effect on the user's cognitive state remains unclear. We collected interaction data between virtual agents and users to observe the mood improvements associated with changes in automatic thoughts that occur in user cognition and addressed the following two points: (1) implementation of a virtual agent that helps a user identify and evaluate automatic thoughts; (2) identification of the relationship between a user's facial expressions and the extent of the mood improvement subjectively felt by users during the human-agent interaction. We focus on these points because cognitive restructuring by a human therapist starts by identifying automatic thoughts and seeking sufficient evidence to find balanced thoughts (evaluation of automatic thoughts). Therapists also use such non-verbal behaviors as facial expressions to detect changes in a user's mood, which is an important indicator for guidance. Based on the results of this analysis, we provide a technical guidance framework that fully automates the identification and evaluation of automatic thoughts to achieve a virtual agent that can interact with users by taking into account their verbal and non-verbal behaviors in face-to-face situations. This research supports the possibility of improving the effectiveness of mental health care in cognitive restructuring using virtual agents.journal articl
Rec-CNN: In-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots
Get PDFA controller area network (CAN) is a communication protocol for in-vehicle networks. Communication between electronic control units (ECUs) is facilitated by the CAN bus. This communication protocol provides no authentication or encryption to prevent the consequences of cyberattacks. As a security measure for this protocol, we have proposed an intrusion detection system (IDS) using a convolutional neural network (CNN). The CNN is trained on recurrence images generated from the encoded labels of CAN frame arbitration IDs, thus Rec-CNN. Using recurrence plots helps us capture the temporal dependency in the sequence of arbitration IDs unlike the state-of-art method, which does not capture this information. We have tested the proposed method on a publicly available dataset with denial of service (DoS), fuzzy, spoofing-gear, and spoofing-RPM attacks, resulting in an accuracy of 0.999. Furthermore, we have experimented with the method on our target vehicle. The proposed method can classify our simulated attacks with an accuracy of 0.999 in an attack frequency of 10 ms.journal articl
Kaggle熟練度に着目したデータ分析プログラム実装におけるソースコード再利用方法の探索的分析
Get PDFデータ分析技術は情報社会における重要な技術のひとつである．世界規模のデータ分析コミュニティである Kaggle では，データ分析技術を競うコンペティションの実施や，データ分析に使用したプログラムの公開，共有が行われている．データ分析プログラムには，プログラムの再利用がコピー＆ペーストで行われるという特徴がある．一方で，類似するプログラム片を複数箇所に記述すると，保守性の低下を招くと言われており，これを防ぐためには関数の定義やライブラリの活用が必要である．しかし，データ分析プログラムの実装において，プログラムの再利用は容易だが，保守作業が困難であるという報告があり，保守性を保ちながらプログラムを再利用するのは難しいと考えられる．本研究では，データ分析の熟練者はプログラムを適切に再利用しているという仮説に基づいて，Kaggle で定義されている熟練度の異なる作者が作成したプログラムに対して，類似するプログラム片の割合やライブラリの利用方法を比較する分析を行った．その結果，熟練度が高い作者ほど関数を多く定義し，類似するプログラム片の割合が少ない傾向にあることがわかった．また，使用されるライブラリの種類に大きな差異は見られなかった．これらの結果から，プログラム片の再利用の観点でデータ分析の初学者が熟練者に近づくには，多くのライブラリを学習することより，類似する処理がある場合には自作関数を定義するような工夫が重要であると考えられる．technical repor
Applying Meta-Learning and Iso Principle for Development of EEG-Based Emotion Induction System
No full textMusic is often used for emotion induction. ince the emotions felt when listening to it vary from person to person, customized music is required. Our previous work designed a music generation system that created personalized music based on participants' emotions predicted from EEG data. Although our system effectively induced emotions, unfortunately, it suffered from two problems. The first is that a long EEG recording is required to train emotion prediction models. In this paper, we trained models with a small amount of EEG data. We proposed emotion prediction with meta-learning and compared its performance with two other training methods. The second problem is that the generated music failed to consider the participants' emotions before they listened to music. We solved this challenge by constructing a system that adapted an iso principle that gradually changed the music from close to the participants' emotions to the target emotion. Our results showed that emotion prediction with meta-learning had the lowest RMSE among three methods (p < 0.016). Both a music generation system based on the iso principle and our conventional music generation system more effectively induced emotion than music generation that was not based on the emotions of the participants (p < 0.016).journal articl
「なでながら話す」マルチモーダルインタラクティブエージェントのVR外見の変化と心地良さの印象評価
Get PDF本研究では，心地良いマルチモーダルインタラクティブエージェント実現のため， バーチャルリアリティ（VR）技術による，なでながら話すロボットの外見を仮想的に変化させ， その変化による心地良さの印象評価を行う． VRとAR（拡張現実感）によるなでながら話すロボットの外見の違いが与える影響について， 従来研究よりVR技術による外見の方が，没入感が高く心地良いという結果から， 本実験でもVR技術を利用した外見の変化を行う． なでる動作を行うためにロボットアームを使っていることよりロボット（人型）と人（男性・女性）， そして擬人化された動物の全4種類のエージェントの外見の印象評価を主観的に行う.technical repor
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
No full textAvoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.conference pape
The Rx transcription factor is required for determination of the retinal lineage and regulates the timing of neuronal differentiation
Get PDFUnderstanding the molecular mechanisms leading to retinal development is of great interest for both basic scientific and clinical applications. Several signaling molecules and transcription factors involved in retinal development have been isolated and analyzed; however, determining the direct impact of the loss of a specific molecule is problematic, due to difficulties in identifying the corresponding cellular lineages in different individuals. Here, we conducted genome-wide expression analysis with embryonic stem cells devoid of the Rx gene, which encodes one of several homeobox transcription factors essential for retinal development. We performed three-dimensional differentiation of wild-type and mutant cells and compared their gene-expression profiles. The mutant tissue failed to differentiate into the retinal lineage and exhibited precocious expression of genes characteristic of neuronal cells. Together, these results suggest that Rx expression is an important biomarker of the retinal lineage and that it helps regulates appropriate differentiation stages.journal articl
PACSIN1 is indispensable for amphisome-lysosome fusion during basal autophagy and subsets of selective autophagy
No full textAutophagy is an indispensable process that degrades cytoplasmic materials to maintain cellular homeostasis. During autophagy, double-membrane autophagosomes surround cytoplasmic materials and either fuse with endosomes (called amphisomes) and then lysosomes, or directly fuse with lysosomes, in both cases generating autolysosomes that degrade their contents by lysosomal hydrolases. However, it remains unclear if there are specific mechanisms and/or conditions which distinguish these alternate routes. Here, we identified PACSIN1 as a novel autophagy regulator. PACSIN1 deletion markedly decreased autophagic activity under basal nutrient-rich conditions but not starvation conditions, and led to amphisome accumulation as demonstrated by electron microscopic and co-localization analysis, indicating inhibition of lysosome fusion. PACSIN1 interacted with SNAP29, an autophagic SNARE, and was required for proper assembly of the STX17 and YKT6 complexes. Moreover, PACSIN1 was required for lysophagy, aggrephagy but not mitophagy, suggesting cargo-specific fusion mechanisms. In C. elegans, deletion of sdpn-1, a homolog of PACSINs, inhibited basal autophagy and impaired clearance of aggregated protein, implying a conserved role of PACSIN1. Taken together, our results demonstrate the amphisome-lysosome fusion process is preferentially regulated in response to nutrient state and stress, and PACSIN1 is a key to specificity during autophagy.journal articl