SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

Jonas Paul David ¹, David Schick ¹, Lorenz Rapp ¹

⁰Zentrum für Zuverlässige Mechatronische Systeme (ZMS), Aalen University, 73430 Aalen, Germany.

Sensors (basel, Switzerland) +

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April 27, 2024

View abstract on PubMed

Summary

A new wearable system collects biomechanical data for exoskeleton design. This cost-effective, all-in-one device accurately measures human knee joint angles during daily activities, aiding in developing assistive technologies.

Area Of Science

Biomechanics
Wearable Technology
Assistive Devices

Background

Exoskeletons for daily living assistance are gaining traction but require further research.
Long-term gait observation in real-world settings is crucial for designing effective exoskeleton systems.

Purpose Of The Study

To propose a wearable, all-in-one data acquisition system for collecting biomechanical data during daily activities.
To develop a cost-efficient and user-friendly system using off-the-shelf components and cloud storage.

Main Methods

A wearable system was developed to collect and store biomechanical data.
System accuracy was verified by measuring knee joint angles against an optical motion analysis system at walking speeds of 3-12 km/h.
Data was uploaded to a cloud database via a smartphone application.

Main Results

The proposed system accurately captured biomechanical data, with Root Mean Square Error (RMSE) ranging from 2.9° to 8°.
This accuracy is comparable to or better than existing systems.
The toolchain demonstrated successful data acquisition and cloud storage.

Conclusions

The developed system offers a powerful and scalable platform for biomechanical data collection and processing.
It facilitates the automated generation of extensive human kinematics databases.
This technology can significantly contribute to the advancement of exoskeleton design and assistive technologies.

Keywords:

active knee orthosis biomechanics exoskeleton human activity recognition medical engineering motion analysis sensor technologies wearable sensors