Embodied Interface
An Arduino-based multimodal hiking interface exploring haptic navigation, visual and auditory status feedback, fall detection through 3-axis accelerometer sensing, and the design of feedback loops that keep a hiker oriented in the environment.
Focus
People
The user is a hiker moving through uneven terrain while balancing attention, fatigue, weather, navigation, and safety. The design has to respect proprioception, interruption cost, and the fact that visual attention is already occupied by the trail.
Product
HapTrek is a wearable safety and navigation concept that uses tactile, visual, and auditory feedback to communicate direction, status, and candidate fall events without requiring a screen-first workflow.
Problem
Outdoor tools often assume phone availability, stable visual attention, and a calm operating context. On a trail, the interface needs to support movement rather than pull the user out of it.
Process
I treated the prototype as a sensor-to-feedback loop: collect motion state, detect meaningful events, map them into learnable feedback patterns, and reason through false positives, escalation behavior, and accessibility constraints.
Product Brief
HapTrek is a coordination problem made tangible: the interface should extend perception into the trail without turning the hike into device management. The key product question is how to design a feedback language that is noticeable, learnable, and trustworthy under fatigue or stress.
Directional cues, safety status, candidate fall events, and environment-aware alerts.
Arduino direction, accelerometer sensing, vibration mapping, and multimodal feedback planning.
False positives, cognitive load, terrain constraints, accessibility, and emergency escalation.
HapTrek is being framed as a professional embodied-interface case study: concept first, then sensor behavior, haptic signal design, safety validation, and accessibility testing.
Define the hiking safety problem as glance-free navigation and terrain-aware status feedback.
Public-safety and non-visual interaction brief.
Use 3-axis accelerometer data to detect motion state and candidate fall events.
Arduino + accelerometer prototype direction.
Map directional and safety states into vibration patterns that can be learned without visual attention.
Vibration language still needs validation.
Test false positives, terrain constraints, cognitive load, accessibility, and response behavior.
Next PRD requirement.
Accelerometer-based state detection, threshold thinking, and fall-detection requirements.
Haptic signal mapping, non-visual information design, and movement-aware interaction constraints.
Accessibility, false-positive analysis, environmental constraints, and emergency escalation planning.