High-Throughput Bench-Testing Platform

• High-throughput bench-testing platform is a purpose-built system that uses synthetic PPG videos and automated test rigs to simulate controlled conditions for smartphone HR measurement.
• It assesses the full signal processing pipeline by comparing measured heart rates against known inputs using metrics like MAPE and Pearson correlation.
• Its scalable design enables rapid, parallel testing of multiple devices, supporting robust device certification and clinical validation.

A high-throughput bench-testing platform is a purpose-built experimental and computational system designed to rapidly, reproducibly, and accurately evaluate the performance of devices, algorithms, or subsystems across numerous configurations or devices in parallel. Such platforms find particular utility in fields where device variability, deployment fragmentation, and the need for rigorous, standardized evaluation preclude manual or low-throughput testing. In the context of smartphone-based heart rate measurement derived from video, the presented platform enables large-scale, parallelized, and standardized assessment of photoplethysmography (PPG) heart rate (HR) apps—addressing significant challenges in reproducibility, device compatibility, and benchmarking fidelity (2506.23414).

1. Platform Purpose and Architecture

The main objective is to streamline pre-deployment validation for smartphone HR measurement apps by simulating controlled, repeatable PPG video input and measuring the device’s and app’s entire signal processing pipeline. The platform’s architecture includes:

• A test rig accommodating up to twelve smartphones arranged facing an inward monitor within a closed box. This configuration permits simultaneous evaluation, enhancing throughput and reducing manual labor.
• Synthetic PPG video generation, where sampled or simulated PPG waveforms (e.g., from MIMIC-III or NeuroKit) are encoded into video sequences. Video frames are constructed so that the mean RGB values correspond to the target PPG waveform at each time point, enabling fine control over HR, signal morphology, and artifacts.
• A host orchestration system that automates test sequencing, video playback across devices, and data collection from the HR monitoring apps.

This design allows uniform exposure of different phones to identical, tightly controlled PPG stimuli, instantiating a high-throughput, end-to-end evaluation pipeline.

2. Synthetic Signal and Video Methodology

Central to the platform is the ability to generate synthetic PPG test videos that interpolate various HRs (e.g., 60 to 180 bpm), brightness levels, pulse amplitudes, and artifact types. For each frame, the pixel values—across all color channels—are drawn to achieve:

$$\text{For each channel } c: \text{Frame}_{(n,m)}^c \leftarrow \mathcal{N}(\mu = \text{PPG value}, \sigma^2)$$

where $\mathcal{N}$ is a normal distribution, ensuring the spatial mean of each frame matches the input waveform. This approach allows the simulation of a wide variety of physiological and device-dependent conditions—including skin pigmentation and brightness, as well as synthetic noise or motion artifacts for stress-testing.

Test waveforms can be systematically varied, enabling platform users to probe the full operational envelope of target algorithms and hardware.

3. Testing Protocol and Evaluation Metrics

The protocol involves:

• Video playback: The platform plays a suite of pre-generated PPG videos to all phones concurrently, each running the HR app under test.
• Automated data collection: The app outputs (measured HR and raw PPG signals) are harvested for each phone and precisely mapped to the known ground-truth HR of the input signal.
• Analysis: Core evaluation criteria include:
  • Mean Absolute Percentage Error (MAPE), defined as:

  $$\text{MAPE} = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{\text{Measured}_i - \text{Input}_i}{\text{Input}_i} \right| \times 100\%$$

  measuring HR accuracy against ANSI/CTA standards (MAPE < 10% required). - Pearson correlation coefficient (r) between the input and recovered PPG waveform:

  $$r = \frac{\sum (X - \overline X)(Y - \overline Y)}{\sqrt{\sum (X - \overline X)^2 \sum (Y - \overline Y)^2}}$$

  quantifying morphological fidelity. - Accuracy and consistency are further assessed by the coefficient of variation for MAPE and r.

4. Empirical Performance and Clinical Correlation

On reference devices, platform results demonstrated a MAPE of 0.11% ± 0.001% and r = 1.0 for HR measurement, with waveform morphology correlations of $r = 0.92 \pm 0.008$. Bench-testing on 20 different smartphones consistently classified all models as ANSI/CTA-compliant (MAPE < 10%). Subsequent clinical validation with 74 participants (diverse in skin tone and age) showed MAPE values ranging from 1.74% to 5.11%—slightly higher than bench results due to real-world artifacts, but confirming the platform’s ability to reliably predict clinical pass/fail status.

This demonstrates the high positive predictive value of bench-testing for regulatory compliance and supports the platform’s utility in device certification and performance assurance.

5. Comparative Features and Innovations

Relative to prior approaches, such as manual clinical trials or software-only algorithmic evaluations:

• Throughput: The rig enables simultaneous assessment of 12+ devices, vastly surpassing single-device or software-only throughput.
• End-to-end Pipeline Evaluation: Tests the full chain—hardware acquisition, OS-layer processing, image signal processing, and app algorithms—not just HR software in isolation.
• Repeatability and Standardization: Synthetic stimuli ensure controlled, repeatable, and artifact-injectable test conditions, unattainable in live testing.
• Scalability: Amenable to expansion for larger device sets, permitting comprehensive device compatibility testing across diverse hardware.
• Integrative Data Logging: Centralized control and logging facilitate rapid analytical cycles and regression testing with each app or firmware update.

A summary table is given below:

Aspect	This Platform	Existing Methods
Throughput	High (12+ devices parallel)	Single-device/manual
Signal Origin	Synthetic video (controllable HR/artifact)	Real human or offline data
Pipeline Coverage	Full HW+SW	SW-only or HW-only
Standardization/Repeatability	High	Low
Artifact/Condition Modeling	Extensive	Limited
Device Coverage	Scalable	Limited by manual effort
Clinical Relevance	Proven predictive value	Variable

6. Limitations and Prospects

While the system cannot simulate the precise human-device interface (e.g., finger pressure, positioning, LED/tissue interaction), and bench-tests are subject to less real-world artifact than clinical use, the design supports the synthetic addition of noise and artifacts for expanded coverage. The current dataset contained no misclassified (false negative) devices, but further validation with broader hardware may refine sensitivity and specificity metrics.

Areas for future improvement include enhanced physical simulation (e.g., robotic finger models), augmented simulation of LED properties and tissue effects, and integration with automated artifact generation for stress-testing.

7. Applications and Domain Impact

The platform enables:

• Rapid pre-deployment validation of HR monitoring apps across the fragmented smartphone ecosystem.
• Automated hardware compatibility testing for manufacturers and developers—accelerating app iteration cycles and facilitating large-scale certification.
• Diagnostic analysis pinpointing device-specific failures, sensor inadequacies (e.g., frame rates), or pipeline regressions before clinical or user exposure.
• Standard development in mobile health by providing a scalable, reproducible, and vendor-independent test methodology.

This approach closes the gap between agile software development and regulatory/methodological rigor in wearable and smartphone-based health monitoring, with direct implications for the safety and efficacy of mobile health technologies.