Human motion tracking
Nodes is a motion capture system intended for medical/research purposes to measure and record the orientation of human body segments. When the system is placed on a body segment, it estimates its orientation in the 3D space and the motor skills parameters related to the examined body part can be computed. When the system is connected to two or more consecutive body segments (interconnected by joints), it’s possible to measure and record relative orientations among segments as well as angles between joints.
Intended use – research, clinical and home rehabilitation
The device is intended to be used for treatment and rehabilitation of patients that suffer from an injury or handicap. The data collected is made available to a medical professional who can use it to support his diagnosis and/or therapy plan or to gain insights into a patient’s mobility capacity.
This project was made possible by a contribution from the European Regional Development Fund as part of OP-Zuid. For more information see EFRO.
Our focus areas
Highly accurate sensors using state of the art sensor fusion algorithms.
Mobile & modular
Single sensor up to full body sensor suits with 20 sensors. Direct streaming to mobile devices.
Quick & easy to use
Fast setup for both home rehabilitation and clinical use.
Low latency real time streaming of quanterion or raw data.
Affordable technology for a wide audiences.
Healthcare is increasingly moving into the living room, and it is also being stimulated that people take more and more control over their own lives. In addition, exercise and sports are becoming increasingly important in healthcare, especially now that the focus is much more on prevention. Also in case of illness (e.g. stroke) or after surgery it is expected that you work on recovery in the home environment. However, the recommended exercises and thus recovery often do not take place because patients are misinformed, guidance is kept to a minimum and they are not motivated to work on their recovery.
The aim of the NODES project is to make responsible movement and exercise “fun and motivating”, while giving care providers a good insight into the exercises that have been done to determine the next steps for recovery.
To achieve this, two technology partners and four application partners consisting of hospitals, physiotherapy practises and sport school collaborate to build, improve and validate a complete motion capturing solution.
Virtual Reality rehabilitation & fitness gaming
With this development, the participants respond to current trends and themes within care such as self-direction, self-management and care within the home environment.
Connect 1-5 NODES sensor strings to a main unit (up to 20 sensors). Main unit communicates wireless to PC with Matlab/Unity.
- Jacket & trousers / complete suit for home rehabilitation
- Strap based solution: clinical use
- Body adhesives: clinical use
- Raw 9DoF data (accelerometer, gyroscope, magnetometer)
- Quaternions, integrated bio-mechanical model (18 segments)
- 33Hz (wireless BLE, with 19 Nodes)
- Up to 100Hz (1 string wired connection)
Approximately 3-7 minutes (depending on wearable solution)
6h (19 nodes connected and wireless on)
- Intel i5, 1.8GHz, 4Gb RAM or more
- Built-in or external wireless BLE module
Windows 10 and later (x64)
- Sensor port: connect 1-5 strings – micro USB
- Charging port: USB-C (5V, 2A)
- Wireless BLE data streaming
Buttons / Indicators
- Power on/off button: yes
- Battery level: yes
- Wireless connect: yes
- String indicator: Left Arm, Right Arm, Left Leg, Right Leg, Trunk
- Waterproof: yes
- Sensor connect to main unit: micro USB
- Sensors per string: 1-4 pcs
- Waterproof: yes
Raw data or Quaternions
each sensor’s table presents 4 columns and as many rows as number of samples collected.
Raw measurement data
each sensor’s table presents 9 columns and as many rows as number of samples collected.
Accelerometer Data are reported in g. Gyroscope data are reported in rad/s. The magnetometer data are reported in G.
Fantazm / Inmotion VR
Erasmus Medical Centre / Rijndam