565 citations · 861 across the 16 of their papers we have counts for
17 papers
TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition
Aidan Bradshaw, Riku Arakawa, Xin Liu +1
Fine-grained wrist activity recognition can support applications such as procedural step guidance and context-aware assistance, yet acquiring labeled data for every new task, user,…
HiFiGaze: Improving Eye Tracking Accuracy Using Screen Content Knowledge
Taejun Kim, Vimal Mollyn, Riku Arakawa +1
We present a new and accurate approach for gaze estimation on consumer computing devices. We take advantage of continued strides in the quality of user-facing cameras found in e.g.…
Negative to Positive Co-learning with Aggressive Modality Dropout
Nicholas Magal, Minh Tran, Riku Arakawa +1
This paper aims to document an effective way to improve multimodal co-learning by using aggressive modality dropout. We find that by using aggressive modality dropout we are able t…
PrISM-Observer: Intervention Agent to Help Users Perform Everyday Procedures Sensed using a Smartwatch
Riku Arakawa, Hiromu Yakura, Mayank Goel
We routinely perform procedures (such as cooking) that include a set of atomic steps. Often, inadvertent omission or misordering of a single step can lead to serious consequences,…
Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership Growth
Riku Arakawa, Hiromu Yakura
Chatbots' role in fostering self-reflection is now widely recognized, especially in inducing users' behavior change. While the benefits of 24/7 availability, scalability, and consi…
IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds
Vimal Mollyn, Riku Arakawa, Mayank Goel +2
Tracking body pose on-the-go could have powerful uses in fitness, mobile gaming, context-aware virtual assistants, and rehabilitation. However, users are unlikely to buy and wear s…
CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models
Riku Arakawa, Hiromu Yakura, Masataka Goto
CatAlyst uses generative models to help workers' progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers…
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching
Riku Arakawa, Hiromu Yakura
In this paper, we discuss the potential of applying unsupervised anomaly detection in constructing AI-based interactive systems that deal with highly contextual situations, i.e., h…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
CalmResponses: Displaying Collective Audience Reactions in Remote Communication
Kiyosu Maeda, Riku Arakawa, Jun Rekimoto
We propose a system displaying audience eye gaze and nod reactions for enhancing synchronous remote communication. Recently, we have had increasing opportunities to speak to others…
AI for human assessment: What do professional assessors need?
Riku Arakawa, Hiromu Yakura
Recent organizations have started to adopt AI-based decision support tools to optimize human resource development practices, while facing various challenges of using AIs in highly…
Semantic constraints to represent common sense required in household actions for multi-modal Learning-from-observation robot
Katsushi Ikeuchi, Naoki Wake, Riku Arakawa +2
The paradigm of learning-from-observation (LfO) enables a robot to learn how to perform actions by observing human-demonstrated actions. Previous research in LfO have mainly focuse…
Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation
Riku Arakawa, Hiromu Yakura
Explicitly alerting users is not always an optimal intervention, especially when they are not motivated to obey. For example, in video-based learning, learners who are distracted f…
A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations
Naoki Wake, Riku Arakawa, Iori Yanokura +4
A household robot is expected to perform various manipulative operations with an understanding of the purpose of the task. To this end, a desirable robotic application should provi…
Exploration of Reinforcement Learning for Event Camera using Car-like Robots
Riku Arakawa, Shintaro Shiba
We demonstrate the first reinforcement-learning application for robots equipped with an event camera. Because of the considerably lower latency of the event camera, it is possible…
REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection
Riku Arakawa, Hiromu Yakura
Executive coaching has been drawing more and more attention for developing corporate managers. While conversing with managers, coach practitioners are also required to understand i…
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback
Riku Arakawa, Sosuke Kobayashi, Yuya Unno +2
Exploration has been one of the greatest challenges in reinforcement learning (RL), which is a large obstacle in the application of RL to robotics. Even with state-of-the-art RL al…