Yuta Sugiura

In this study, we propose the toolkit for exploring the ambient interaction using plants. Our toolkit can be attached to existing plants or artificial greenery, and generate the expressive plants movement by using a build-in motor. Our toolkit can generate three type of natural motions (vibration, water droplets, and airflow), supporting designers for designing ambient information system. We investigated the usability, acceptability, and potential application of the proposed device through user study. The results show the toolkit's high usability and the potential for creating numerous applications. Moreover, A two-week field study revealed that applications created using the toolkit demonstrated high acceptability.

Research and design in Human-Computer Interaction (HCI) have increasingly engaged with embodied, situated, and material-mediated forms of interaction. Across many such systems, sensing is not performed solely by embedded, off-the-shelf components, but arises through material properties and transformations. Yet HCI still lacks a coherent framework for articulating how materials themselves participate in sensing beyond serving as passive substrates. This paper introduces Ambient Material-Embodied Sensing (AMES), a generative theoretical framework that reframes sensing by foregrounding materials as the sensing medium in their own right. AMES conceptualizes it as an emergent property of material transduction, material-body coupling, and ambient computational inference. We articulate three core mechanisms and demonstrate how AMES can reinterpret existing systems while informing future design and research on material-mediated sensing in interaction design.

Background: Grip strength is a crucial indicator of muscle deterioration, recovery, sarcopenia, and neurological disorders. However, conventional measurement requires a dedicated dynamometer, which limits accessibility and requires specific movements. Objective: This study aimed to propose and validate a method for estimating grip strength using standard smartphone operations, thereby eliminating the need for specialized equipment. Methods: Data were collected from 21 young adults in the main experiment, who performed standard smartphone tasks (tapping, flicking, and dragging) after measuring their grip strength with a dynamometer. A predictive regression model was developed using touch and inertial sensor data. The model was first evaluated using a random split of the entire dataset (random split evaluation). To further assess practical feasibility and generalizability, we conducted leave-one-user-out validation and a few-day calibration validation; the latter simulated a calibration scenario by incorporating 1 to 4 days of user-specific data into the training set. Results: The regression analysis using a random split of the dataset demonstrated high accuracy, with a mean absolute error of 2.62 (SD 0.18) kg, a mean absolute percentage error (MAPE) of 8.91% (SD 0.57%), and a coefficient of determination of 0.802 (SD 0.036). In the validation of practical scenarios, the leave-one-user-out validation resulted in a MAPE of 15.08% (SD 5.40%). However, the personalized few-day calibration model showed significant improvements as calibration days increased, with the MAPE decreasing to 13.96% (SD 5.57%) after 1 day and reaching 11.64% (SD 5.80%) after 4 days. Furthermore, the National Aeronautics and Space Administration Task Load Index assessment indicated a low overall subjective workload (mean 3.04, SD 2.23 on a scale of 10), confirming the method’s suitability for daily use without a significant burden on users. Conclusions: The proposed method demonstrates that smartphones can serve as a viable, pervasive tool for daily grip strength monitoring, offering a convenient alternative to traditional dynamometers.