Tangible Devices for Low-Effort,

Participant-Controlled Data Input

Resaerch Goal

In this work, we explore tangible interaction as a potential intermediate data collection approach that could reduce capture effort while supporting participant agency and contextual relevance

Motivation

Personal data collection in HCI is often described as falling at two extremes. On one side, self-report methods such as diary studies and ecological momentary assessment can offer rich contextual data, though they may introduce participant burden and adherence challenges.
On the other hand, passive sensing approaches can provide continuous capture but may raise questions about interpretability, alignment with participants’ understanding, and the degree of control afforded.

Design Goals

Prior work shows recurring tradeoffs in personal informatics: manual diaries and apps support rich, user-driven self-report but take effort; passive sensing lowers burden but shifts control to the system; micro interaction tools reduce effort but stay screen based and constrained; and flexible platforms enable user defined tracking but often still rely on screens or multiple devices. From these tensions, we derived the following design considerations for our tangible prototypes.
DC1. Preserve agency while reducing per-entry effort
Entries should always be user initiated, but fast. Our designs should support one to two-second physical actions so people stay in control without the overhead of forms or menus.

DC2. Embed interactions in everyday places and movements
Tracking should fit naturally into routines and spaces. We should design for touch first, eyes free interactions that work where experiences happen, not only through a phone screen.

DC3. Support user defined and evolving tracking categories
People need flexibility to define what matters and change it over time. Our devices should expose configurable channels that can be repurposed as goals and contexts evolve.

DC4. Keep data streams simple, structured, and transparent
Logs should be easy to understand and trust. Each interaction should produce a clear record, such as a timestamp, channel, and a small parameter like duration or strength, mirrored back in human-readable form.

DC5. Fit into everyday life as approachable probes
Adoption depends on materials, aesthetics, and domestic fit. We should design the artifacts feel like everyday objects and treat appropriation, placement, and customization as part of what we learn.

The Portable Drop

The Drop is a tear drop shaped artifact designed to move with the user throughout the day. It can be worn as a necklace or clipped to keys or a bag, and it runs on a rechargeable battery.

Drop supports quick, eyes free tracking through three interaction areas. Each area can be assigned to a specific tracking category in the companion app. For example, if the top area is defined as a pain entry, the user can press and hold that area when pain occurs. The timestamp records when the event happened, and the hold duration can be used to represent intensity. Each interaction is stored as a single entry in the app, including the assigned category and the values captured through the gesture.

The punchboard

This stationary artifact is designed to support tracking in a specific place, helping the user collect data as it naturally occurs. The user can place it where they spend most of their time or where the target experiences happen, for example near a workstation to track work related moments, or in a living room to capture daily routines or social activities. The board can be moved and re situated as needed.

The user interacts with the board through simple gestures such as punching, tapping, or swiping. Each gesture can be defined as a specific data entry type in the app. When the user performs an interaction, the system records when it happened, and when relevant, how it happened, such as the number of taps or the strength of a punch. For example, a left to right swipe could be defined to log tiredness, a right to left swipe to log energy, and a punch to mark task completion. Each interaction then appears in the app labeled with the user defined data type.

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