Portable Biomechanics Laboratory (PBL)

• Portable Biomechanics Laboratory (PBL) is a deployment paradigm that integrates portable sensing, synchronized acquisition, and computational pipelines to quantify human movement outside fixed labs.
• They employ diverse hardware—from smartphone video and wearable IMUs to force-sensitive insoles and exoskeleton load cells—to capture kinematics, kinetics, and postural assessments.
• Validated against lab-grade benchmarks, PBL systems demonstrate errors typically below 5° in joint angles and within 10–15% accuracy, ensuring accessible and cost-effective biomechanical analysis.

Portable Biomechanics Laboratory (PBL) is a term used in recent biomechanics literature for portable systems that combine lightweight sensing, synchronized acquisition, and computational biomechanics pipelines to quantify movement, loading, or human–robot interaction outside conventional fixed laboratories. Reported PBL implementations span smartphone video, wearable IMUs, force-sensitive insoles, Wii Balance Board ground-reaction-force acquisition, optical-fibre balance mats, portable strain-gauge spine sensors, exoskeleton-integrated load cells, and cloud-connected monocular model fitting. Across these systems, the portable laboratory function is not limited to kinematics; it also includes postural sway, muscle-force estimation, gait-phase detection, spinal curvature, and exoskeleton-user interface force measurement (Hasan et al., 2021, Ren et al., 1 Mar 2025, Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026).

1. Conceptual scope and development

An early explicit PBL formulation was presented for lifting-related injury analysis in manual material handling, where a portable and low cost system combined a smartphone camera, a Wii Balance Board, Kinovea, Brainblox, and OpenSim Static Optimization to move from joint-angle collection to muscle-force estimation. In that configuration, the stated motivation was the impracticality of multi-camera motion capture, force plates, and electromyography systems in developing countries, and the resulting workflow was a fully portable, sub-\$1,000 platform for lifting analysis (Hasan et al., 2021).

Subsequent work broadened both the sensing substrate and the intended deployment environment. One branch integrated a compact load cell into the thigh cuff of a back support exoskeleton for workplace evaluation; another defined PBL as clinically accessible movement analysis from handheld smartphone video; others realized PBL through a single smartphone and uncalibrated IMUs, an optical-fibre Balance Mat, a portable strain-gauge spine system, a modular exoskeleton sensor stack, and a fully wearable machine-learning framework for real-time joint kinematics, kinetics, and ground reaction force prediction (Ren et al., 1 Mar 2025, Peiffer et al., 2024, Shrestha et al., 25 Jul 2025, Suter et al., 2019, Marinou et al., 2024, Mallah et al., 26 Jan 2026, Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026).

A common misconception is that PBL denotes a single hardware configuration. The literature instead uses the term for a family of architectures that share a portability constraint while differing substantially in sensing modality, model structure, and target variable. This suggests that PBL is best understood as a deployment paradigm in biomechanics rather than as a single instrument design.

2. Sensing architectures and hardware modalities

Smartphone-centered PBL systems occupy one major branch. In the lifting-analysis system, a modern phone capable of recording at $\ge 25\,\mathrm{Hz}$ and at least $720\mathrm{p}$ resolution was sufficient for sagittal-plane kinematics when paired with Kinovea. In the IMU–video fusion system, a Samsung Galaxy S20 provided RGB video at $1080\times1920$ pixels at $30\,\mathrm{Hz}$ together with an internal gyroscope at $100\,\mathrm{Hz}$; in the clinically deployed handheld smartphone PBL, Android phones such as the Samsung S8 and S20 Ultra recorded $1080\times1920$ RGB video at $30\,\mathrm{Hz}$, optional depth video, built-in IMU streams, and optional synchronized wearable IMUs; and in OpenCap Monocular, an iPhone or iPad released since 2018, mounted statically on a tripod, served as the sole acquisition device for monocular 3D kinematics and kinetics (Hasan et al., 2021, Peiffer et al., 2024, Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026).

Wearable and embedded PBL systems define a second branch. A gait-oriented wearable implementation used a Central Unit with Arduino Nano, Real-Time Clock, SD card socket, a $5\,\mathrm{V}$ power bank, and up to four limb modules populated with MPU-6050 IMUs, yielding a total weight below $1.5\,\mathrm{kg}$ and a form factor that fits into a $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$ backpack. A lower-limb exoskeleton system used Bosch BNO055 IMUs, FUTEK LCM200 load cells with HX711 amplification, generic FSRs embedded in TPU insoles, Adafruit ESP32-S3 Feather boards, and a Raspberry Pi 4 central unit. A separate real-time wearable framework used EmotiBit IMUs communicating by UDP over Wi-Fi together with FSR-based sandal insoles, with donning time of approximately $720\mathrm{p}$0 minutes and battery life of at least $720\mathrm{p}$1 hours per session (Meghapathirana et al., 2024, Marinou et al., 2024, Mallah et al., 26 Jan 2026).

Force- and pressure-oriented PBL hardware also appears in several forms. The Balance Mat measured $720\mathrm{p}$2, weighed $720\mathrm{p}$3, sampled at $720\mathrm{p}$4, and used a grid of plastic optical fibres whose light intensity changed under foot pressure; a microcontroller digitized, time-stamped, and streamed the resulting voltage signals over USB without an external power supply. The Epionics SPINE system used two flexible sensor stripes placed $720\mathrm{p}$5 lateral to the spinous processes, each carrying $720\mathrm{p}$6 strain-gauge bending sensors connected to a lightweight storage unit of $720\mathrm{p}$7 and $720\mathrm{p}$8. In workplace exoskeleton evaluation, the PBL sensor module was a TE Connectivity FX29K0-100A-0100-L load cell integrated into the thigh cuff; its full-scale range was $720\mathrm{p}$9 ($1080\times1920$0), its size was $1080\times1920$1, its mass was $1080\times1920$2, and the total sensor-module mass per leg was below $1080\times1920$3 (Shrestha et al., 25 Jul 2025, Suter et al., 2019, Ren et al., 1 Mar 2025).

Data transport and power management vary by system but are consistently designed for mobile operation. Examples include Bluetooth communication from the Wii Balance Board, CAN and UART streaming from exoskeleton load-cell electronics, BLE JSON packet transport in modular exoskeleton sensing, USB-powered acquisition in the Balance Mat, local SD logging in IMU modules, and secure cloud upload with database provenance in handheld smartphone PBL deployments (Hasan et al., 2021, Ren et al., 1 Mar 2025, Marinou et al., 2024, Peiffer et al., 11 Jul 2025).

3. Modeling, synchronization, and inference pipelines

Classical biomechanics pipelines remain central in several PBL implementations. In the lifting-analysis system, joint angles were computed from tracked 2D segment vectors as

$1080\times1920$4

and OpenSim was used to solve inverse dynamics and static optimization for muscle forces under the standard equations of motion

$1080\times1920$5

In the gait-wearable PBL, raw IMU data were processed with an Extended Kalman Filter whose state vector contained a sensor-orientation quaternion and gyroscope biases, and joint angles were then obtained from relative segment quaternions (Hasan et al., 2021, Meghapathirana et al., 2024).

Exoskeleton-integrated PBL sensing uses explicit measurement models and tightly specified synchronization. The thigh-cuff load-cell system modeled the local interface force as a linear function of the raw digital reading, reducing in practice to

$1080\times1920$6

with $1080\times1920$7 as full-scale count range and $1080\times1920$8. Signal conditioning included a 4th-order Butterworth low-pass filter with cut-off $1080\times1920$9, while all I$30\,\mathrm{Hz}$0C readings were time-stamped on the IMU board at $30\,\mathrm{Hz}$1 intervals and polled on the main processor in matching $30\,\mathrm{Hz}$2 time slots. Epoch events were triggered by peak trunk IMU pitch and by box-impact IMU on the load (Ren et al., 1 Mar 2025).

Differentiable model fitting is the dominant strategy in recent camera-based PBL systems. In the IMU–video fusion framework, a multi-layer perceptron $30\,\mathrm{Hz}$3 mapped time to pose and camera orientation, a MuJoCo biomechanical model produced $30\,\mathrm{Hz}$4 keypoints and $30\,\mathrm{Hz}$5 segment orientations, and optimization minimized a cost containing keypoint, reprojection, attitude, sensor-gyroscope, and phone-gyroscope terms while learning the sensor-to-body rotations online. The handheld smartphone PBL likewise parameterized each trial by a small MLP mapping time to joint angles $30\,\mathrm{Hz}$6 and camera orientation $30\,\mathrm{Hz}$7, and optimized weighted 2D, 3D, and phone-orientation losses through a differentiable MuJoCo forward-kinematics engine using JAX and Equinox. OpenCap Monocular used WHAM for monocular pose initialization, ViTPose for 2D keypoints, two consecutive batch optimizations in PyTorch, a $30\,\mathrm{Hz}$8-DOF OpenSim model for inverse kinematics, and a hybrid kinetics stack comprising physics-based simulation, Hunt–Crossley foot–ground contact, direct collocation via CasADi and IPOPT, and a machine-learning module termed GaitDynamics (Peiffer et al., 2024, Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026).

Real-time PBL systems emphasize low-latency inference rather than global batch fitting. In the exoskeleton-control architecture, sensor normalization and sigmoid membership functions fed an eight-phase fuzzy rule base, and the anteroposterior center of pressure was computed from heel and metatarsal forces. In the wearable machine-learning system, Random Forests predicted joint angles from IMU features, a separate Random Forest predicted vertical ground reaction force from FSR inputs, and a ResNet-16 model predicted joint moments from kinematics and force surrogates, with logging at $30\,\mathrm{Hz}$9 and minimal delay suitable for biofeedback (Marinou et al., 2024, Mallah et al., 26 Jan 2026).

4. Validation paradigms and reported accuracy

Validation protocols in PBL research are highly heterogeneous, but most benchmark against established laboratory references. For the exoskeleton load-cell interface, calibration accuracy reached RMSE $100\,\mathrm{Hz}$0 over the $100\,\mathrm{Hz}$1–$100\,\mathrm{Hz}$2 range with repeatability $100\,\mathrm{Hz}$3 across five trials per mass. During stoop lifting and lowering of a $100\,\mathrm{Hz}$4 box, Subject 1 under load showed Pearson correlations of $100\,\mathrm{Hz}$5 and $100\,\mathrm{Hz}$6, both with $100\,\mathrm{Hz}$7; Subject 2 without load showed $100\,\mathrm{Hz}$8 but only $100\,\mathrm{Hz}$9, explicitly interpreted in the report as weaker and indicating fit/parasitics (Ren et al., 1 Mar 2025).

The low-cost lifting PBL validated its pipeline on one healthy subject performing three symmetric lifts of a $1080\times1920$0 dumbbell. Six sagittal-plane joint angles were extracted at $1080\times1920$1; compared qualitatively to high-precision multi-camera data in the literature, deviations in peak angles were below $1080\times1920$2 for all joints except a known elbow-phase shift. Peak vertical GRF was approximately $1080\times1920$3 and Wii-board sensor error was below $1080\times1920$4 when compared to lab-grade platforms. Static optimization yielded peak vastus intermedius force of approximately $1080\times1920$5 and gluteus maximus force of approximately $1080\times1920$6, with timing and magnitude within $1080\times1920$7–$1080\times1920$8 of values reported in the literature for similar lifts. In portable spinal measurement, Suter et al. reported excellent agreement between Epionics SPINE and Vicon for chair rising, box lifting, and countermovement jump, moderate to high reliability for most continuous and discrete parameters, and ICC values at or above $1080\times1920$9 except for ROM during running, where ICC was $30\,\mathrm{Hz}$0 (Hasan et al., 2021, Suter et al., 2019).

Video-centered PBL systems now report error levels close to those expected in markerless clinical biomechanics. In the uncalibrated IMU–video fusion study, knee flexion/extension error versus multi-camera truth improved from video-only MAE-MA of $30\,\mathrm{Hz}$1 to fusion MAE-MA of $30\,\mathrm{Hz}$2, while Pearson $30\,\mathrm{Hz}$3 improved from $30\,\mathrm{Hz}$4 to $30\,\mathrm{Hz}$5 with significance at $30\,\mathrm{Hz}$6; under artificial occlusion of the sensorized leg, video-only tracking degraded to approximately $30\,\mathrm{Hz}$7, whereas fusion remained at approximately $30\,\mathrm{Hz}$8. In the clinically deployed handheld smartphone PBL, median joint-angle error across all clinical populations and tasks was approximately $30\,\mathrm{Hz}$9, with handheld MMMC performance reported as $5\,\mathrm{V}$0 and root translation error of $5\,\mathrm{V}$1. In OpenCap Monocular, rotational MAE was $5\,\mathrm{V}$2, pelvis translational MAE was $5\,\mathrm{V}$3, and vertical walking GRF error was $5\,\mathrm{V}$4 body weight versus $5\,\mathrm{V}$5 for the baseline (Peiffer et al., 2024, Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026).

Balance, exoskeleton-control, and fully wearable ML systems are similarly benchmarked against force plates or motion capture. The Balance Mat robot study reported good to excellent ICC values, with double-stance single-measure ICCs of $5\,\mathrm{V}$6 for Mean, $5\,\mathrm{V}$7 for RMS, $5\,\mathrm{V}$8 for Path, $5\,\mathrm{V}$9 for Range, and $1.5\,\mathrm{kg}$0 for Velocity; in human testing, eyes-open correlations included $1.5\,\mathrm{kg}$1 for AP Path and $1.5\,\mathrm{kg}$2 for AP Range, while eyes-closed correlations included $1.5\,\mathrm{kg}$3 for AP Range and $1.5\,\mathrm{kg}$4 for ML Path. The modular exoskeleton sensor system reported mean Pearson $1.5\,\mathrm{kg}$5 and RMSE $1.5\,\mathrm{kg}$6 for anteroposterior CoP, mean Pearson $1.5\,\mathrm{kg}$7 and RMSE $1.5\,\mathrm{kg}$8 for crutch GRF, and heel-strike timing mean absolute error of $1.5\,\mathrm{kg}$9 with sensitivity of approximately $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$0 and specificity of approximately $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$1. The wearable real-time ML framework reported intra-subject vGRF NRMSE of $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$2, inter-subject vGRF NRMSE of $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$3, and end-to-end delay of approximately $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$4 per new data block (Shrestha et al., 25 Jul 2025, Marinou et al., 2024, Mallah et al., 26 Jan 2026).

5. Clinical, industrial, and field applications

Workplace and human–robot-interaction assessment are prominent PBL use cases. The exoskeleton thigh-cuff interface was explicitly designed to evaluate back support exoskeletons in both laboratory and workplace environments, offering a stable alternative to electromyography and respiratory gas measurements. Its Qwiic and CAN architecture was also presented as a host for additional IMUs on trunk and limb, a portable gas analyser such as COSMED K5 via Bluetooth, and pressure insoles or goniometers. In lower-limb exoskeleton research, modular crutches and 3D-printed insoles were used not only for biomechanical measurement but also for real-time gait-phase estimation and exoskeleton control in the TWIN system (Ren et al., 1 Mar 2025, Marinou et al., 2024).

Clinical deployment is now a central PBL theme. The handheld smartphone PBL was integrated into neurosurgery clinics for cervical myelopathy and sports-medicine clinics for knee osteoarthritis, with setup time below $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$5 minute per patient and no specialized staff required. In cervical myelopathy, gait metrics derived from the PBL correlated with mJOA scores, and the Gait Deviation Index showed a standardized response mean of $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$6 after decompression surgery, with a $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$7 confidence interval that did not cross zero. OpenCap Monocular extended this clinical framing through an iOS smartphone app, web application, secure cloud computing, and an API for programmatic output download, and it reported kinetic accuracy in applications related to frailty and knee osteoarthritis, including knee extension moment during sit-to-stand and knee adduction moment during walking (Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026).

Other PBL deployments target low-resource ergonomics, gait adaptation, balance assessment, and spine monitoring. The lifting-analysis system addressed manual material handling in developing countries; the wearable gait PBL examined the impact of seven shoe conditions on step time, propulsion-force proxies, stability, and joint-angle range of motion; the Balance Mat targeted portable postural stability assessment; and the Epionics SPINE system enabled sagittal lumbar tracking during standing, sitting, chair rising, box lifting, walking, running, and countermovement jump (Hasan et al., 2021, Meghapathirana et al., 2024, Shrestha et al., 25 Jul 2025, Suter et al., 2019).

6. Limitations, calibration demands, and future directions

Portability in PBL does not eliminate calibration; rather, it redistributes calibration into portable workflows. The exoskeleton load-cell report recommended pre-use zeroing, post-task drift checks, optional thermistor-based compensation, and quarterly recalibration. The Balance Mat required linear-regression calibration because Bland–Altman analysis showed proportional bias and systematic overestimation of sway. The wearable IMU gait system specified accelerometer zero-offset, accelerometer scale-factor, gyroscope bias, and optional temperature-compensation procedures. By contrast, the IMU–video fusion method eliminated explicit static pose or sensor-body attachment calibration by learning sensor-to-body rotations online, showing that calibration burden is architecture-dependent rather than universally removed (Ren et al., 1 Mar 2025, Shrestha et al., 25 Jul 2025, Meghapathirana et al., 2024, Peiffer et al., 2024).

Several limitations recur across PBL studies. A single sagittal-plane camera constrains the lifting-analysis system to symmetric lifts unless a second synchronized camera and a 3D reconstruction pipeline are added. The Wii Balance Board measures only vertical force, so horizontal shear forces are assumed negligible in slow lifts but are not captured. OpenCap Monocular identifies monocular depth ambiguity in out-of-plane motions and poor foot-contact probabilities during flight phases. The handheld smartphone PBL reports ankle median joint-angle error of approximately $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$8, occasional heavy tails in the error distribution, and systematically higher errors for some viewpoints. The Balance Mat human validation was limited to healthy young adults, and the authors explicitly called for further validation in elderly or clinical populations (Hasan et al., 2021, Gilon et al., 25 Mar 2026, Peiffer et al., 11 Jul 2025, Shrestha et al., 25 Jul 2025).

Future directions in the literature are correspondingly diverse. Proposed developments include integrated acquisition GUIs for jointly time-stamped video and force data, multi-view $$30\,\mathrm{cm}\times20\,\mathrm{cm}\times10\,\mathrm{cm}$$9 reconstruction, incorporation of IMUs to complement video, real-time confidence intervals for monocular kinematics, extension to multi-view handheld streams, integration of biomechanics-informed dynamics such as Kinetic-Twin methods, on-device inference, Android support for smartphone monocular analysis, wireless real-time feedback for spinal sensing, and combined IMU plus strain-gauge approaches for full-spine curvature mapping (Hasan et al., 2021, Peiffer et al., 11 Jul 2025, Gilon et al., 25 Mar 2026, Suter et al., 2019).

Taken together, the PBL literature shows a convergence of three technical ideas: portable sensing, synchronization-compatible acquisition, and model-based or learning-based inference. The resulting systems do not yet erase the need for validation, calibration, or task-specific assumptions, but they demonstrably relocate substantial parts of biomechanics measurement from specialized laboratories into clinics, workplaces, and field environments.