ANDROID, VISUALIZATION, LIBRARY, MOBILE

SalusChart

Research Period: 2025.03 - Present

Overview

SalusChart is an independent, production-grade mobile-first data visualization library designed for health and tracking data. It is built to produce readable and reliable charts on small screens, and it is integrated as a module inside my modular data tracking application.

Why This Project

Many chart libraries can render charts, but good health visualizations on mobile are difficult without visualization expertise. SalusChart focuses on two practical goals:

  1. Mobile-optimized visualization by default (layout, scaling, touch, readability)
  2. High-quality results even for non-visualization experts, through strong defaults and guided configuration

Problem

  • Mobile chart UX often breaks due to scaling and typography issues across devices.
  • Interactions (scrolling, paging, tooltips, zoom/landscape behavior) are fragile on mobile.
  • Choosing the wrong chart type or unclear styling can make health data misleading or hard to interpret.
  • Real-world tracking data frequently includes gaps, irregular sampling, and time-related complexity, which many general-purpose chart libraries do not handle well.

Design Principles

  • Mobile-first scaling: validate chart layouts across screen sizes and densities.
  • Readable typography: consistent rules for chart titles, axis labels, data labels, and legends.
  • Interaction reliability: paging + scrolling as primary interactions, with stable touch targets.
  • Smart defaults: sensible chart choices and styling so users get “good charts” without needing to be visualization experts.

What I Built / Contributed

  • Led the initial architecture, design guidelines, and core implementation of SalusChart as a standalone library.
  • Implemented mobile interaction foundations centered on paging and scrolling to support exploration on small screens.
  • Established chart UX rules and a checklist based on common mHealth visualization issues (scaling, styling, interactivity, chart type fit).
  • Documented improvement targets and performance notes to guide later optimization and refinements.

Note: I led the early design and implementation phase. The later polishing and final completion were handed off to another contributor.

Chart Coverage (Examples)

SalusChart supports multiple chart patterns for health/tracking data, including:

  • Trend charts (e.g., line/area-style patterns)
  • Bar-style summaries (including stacked patterns)
  • Calendar-style views

  • Domain-specific views such as sleep-stage style representations

    (Exact chart set evolves as the library expands inside the tracking platform.)

Tech and Keywords

Android, Kotlin, Jetpack Compose, Mobile Data Visualization, Custom Chart Components, Progressive Disclosure, Smart Defaults

Outcome

A production-grade mobile visualization library that emphasizes mobile usability and strong defaults. SalusChart makes it easier for teams to create high-quality health charts without requiring a visualization specialist for every design decision.

Future Work

  • Publish deeper performance and design rationale as blog posts (e.g., performance bottlenecks, interaction trade-offs, and “smart default” decisions).
  • Expand integration in the modular data tracking platform to standardize visualization across studies.