Chopsticks: Motion sensing for medical diagnosis and rehabilitation · 2016-10-17 · Chopsticks: Motion sensing for medical diagnosis and rehabilitation Project members from IT:

Chopsticks: Motion sensing for medical diagnosis and rehabilitation

Project members from IT: Edith Ngai (Dept. of IT, UU) Aji John Pushpam (Dept. of IT, UU) Rudolfs Agrens (Dept. of IT, UU) Tim Josefsson (Dept. of IT, UU) Project members from BioMed: Gustaf Gredebäck (Dept. of Psychology and BabyLab, UU) Staffan Karlsson (Dept. of Psychology and BabyLab, UU)

BioMedIT SPARC Fund Project

Outline

• Background • System design • Calibration and motion tracking • Motion classification • Experiment


Background • The accurate measurement of orientation plays a

critical role in a range of fields including: aerospace, robotics, virtual reality, and human motion analysis

• In rehabilitation, motion tracking is vital enabling technology, in particular for monitoring outside clinical or lab environment

• While extensive work has been performed for motion tracking for rehabilitation, existing systems require special special laboratory environment

• An unobtrusive and daily usable system capable of logging data for extended periods of time has yet to be realized


Camera based motion tracking system


Inertial measurement unit (IMU)

• IMU is a combination of accelerometers and gyroscopes, sometimes also magnetometers


Inertial navigation systems (INS)

• The operating principles for measuring orientation and position of a moving body using only gyroscopes and accelerometers


Chopsticks system design (1) Sensors for

motion tracking

(2) Sensor data analysis

(3) Feedback to users



Chopsticks sensor prototype


Data collection

• InvenSense MPU-9250 9-axis motion tracking device



Sensor calibration

• Integrated sensor with accelerometer/gyroscope/magnetometer

• Calibrate offset first, then scale • Accelerometer: use gravity constant on all axis • Gyroscope: first stationary for offset, then

verify turn angle • Fuse both sensor readings for pitch and roll

Noise filtering Sensor noise modeling assumptions:

− Additive white Gaussian noise (AWGN);

− The human movement signal is a baseband signal (most of the signal energy is concentrated around 0-5 Hz), e.g. the speed of up and down movement is less that 5 times/sec.

Requirements on filter design:

− Real time;

− Low computation cost.

Sensor

+

Noise

Movement Measurement Filter

Output


Noise filtering FIR (Finite Impulse Response) filter:

− A low-pass filter with low computation cost;

− The estimated movement is given by the weighted sum of the most recent measurements;

− The window size and coefficients of filter is adjust based on types of movements.


Noise filtering An comparison between original measurements and filtered signal. The

movement is periodic forward and back on the x axis.


Motion tracking algorithm

• The sensor data was first processed through an AHRS algorithm to calculate the orientation of the x-IMU relative to the Earth

• Then, the corresponding direction of gravity could be subtracted from the accelerometer measurements.

• The resultant measurement of acceleration was then integrated to yield a velocity and the velocity high-pass filtered to remove any drift.

• Finally, the filtered data is integrated again to yield a position which was also high-pass filtered to remove drift.


Calibration example

Position estimation

• An object’s orientation and position can be estimated by integrating the gyroscope data and double integrating the accelerometer data in time

• Due to inherent integration drift, the position estimation is correct only within a few seconds

• To estimate accurate position, it has to be combined with other aiding system, such as GPS, ultra-wideband sensors, visual sensors

• It may work better if the motion has to be constrained and repetitive, e.g. with zero velocity updates (when the foot touches the ground), etc.


Motion capture

• Zero velocity updates (when the foot touches the ground) • Related work on human motion tracking [Roetenberg et al.

2013, Kok et al. 2014]


Experiment setup

• MCU-6050 chip • Matlab, Processing and Arduino (C++) • Low-pass filter accelerometer data • High-pass filter velocity / position data • Gravity compensation algorithm • Accelerometer and gyro fusion

Toothbrush demo

Motion tracking system workflow


Sensor calibration

Data collection

Noise filtering

Motion classification Motion tracking

Motion classification

• Build model from collected data – Each movement pattern trains the model

• Apply the model on a user’s input – Classify the samples

• Analyze the classification results


Tool used:

Motion classification


• Decision tree for the classification – Traverse the tree until a end-node

is reached.

• Currently done for each sample

Results

L. McNamara and E. C.-H. Ngai, SADHealth: A Personal Mobile Sensing System for Seasonal Health Monitoring, accepted in IEEE Systems Journal, 2016.

Validating our model, how accurate is it?

Analyzing the classification

What can we do with the classification?

Conclusions and future work

• Motion classification obtains high accuracy using only accelerometer and gyroscope data

• Position estimation needs more constrained settings or additional sensors to improve accuracy

• Future works include improving motion tracking accuracy, and data collection is on-going in the BabyLab

• Extension and testing in other application domains • Funding opportunities from EU, Vinnova, and for

clinical applications, etc.


Documents

Chopsticks: Motion sensing for medical diagnosis and rehabilitation · 2016-10-17 · Chopsticks: Motion sensing for medical diagnosis and rehabilitation Project members from IT: