Haptify: A Measurement-Based System for Quantifying the Quality of Haptic Interfaces

Grounded force-feedback (GFF) devices, exoskeletons, and other haptic robots modulate human movement through carefully engineered mechanical, electrical, and computational designs. Given their significant societal potential and often high cost, it is essential to fairly and efficiently assess the quality of these intimate cyber-physical interfaces. However, existing device specifications and low-level performance metrics often fail to capture the nuanced qualities that expert users perceive during hands-on experimentation. To address this gap, this thesis introduces Haptify, a comprehensive benchmarking system that can thoroughly, fairly, and noninvasively evaluate GFF haptic devices. Haptify integrates multiple sensing modalities - a seven-camera optical motion-capture system, a custom-built 60-cm-square force plate, and an instrumented end-effector that can be adapted to different devices - to record the interaction between the human hand, the device, and the ground during both passive and active experiments. With this setup, users hold the device end-effector and move it through a series of carefully designed tasks while Haptify measures kinematic and kinetic responses. From this process, we establish six key ways to assess GFF device performance: workspace shape, global free-space forces, global free-space vibrations, local dynamic forces and torques, frictionless surface rendering, and stiffness rendering. These benchmarks enable systematic evaluation and comparison across devices. We first apply Haptify to benchmark two GFF devices produced by 3D Systems: the widely used Touch and the more expensive Touch X. Results reveal that the Touch X offers a slightly smaller workspace than the Touch, but it produces smaller and more predictable free-space forces, reduced vibrations, more consistent dynamic forces and torques, and higher-quality rendering of both frictionless surfaces and stiff virtual objects. To further validate and extend our approach, we conducted a user study with sixteen expert hapticians who used Haptify to evaluate four commercial GFF devices: Novint Falcon, Force Dimension Omega.3, Touch, and Touch X. Experts tested the devices in unpowered mode and across five representative virtual benchmark environments, providing extensive quantitative ratings and qualitative feedback. We distilled recurring themes from their input and analyzed correlations between expert opinions and sensor-based measurements. Our findings show that expert judgments of fundamental haptic quality indicators align closely with the metrics derived from Haptify. Moreover, device performance both unpowered and in active benchmarks can be used to predict its suitability for more complex applications, such as teleoperated surgery. By linking expert assessments with external measurement data, this thesis establishes a combined qualitative-quantitative framework for benchmarking haptic robots. This approach not only enables fair comparison across diverse devices but also establishes a direct connection between objective measurements and the subjective expertise of experienced hapticians. In doing so, it lays the foundation for more rigorous, transparent, and application-relevant evaluation of haptic technologies.