Copyright ©2020 All Rights Reserved

**Luke Wicent Sy**, Nigel Lovell, Stephen Redmond

Osteoarthritis

Cerebral Palsy Surgery

Parkinson's Disease

Perform. Improvement

Fall risk assessment

Real Time Feedback

Very accurate but limited to a small space

Miniaturization. Track position and orientation (albeit with drift).

Can capture almost everywhere. Can be conspicuous for everyday use

More comfortable

- Soft stretch sensors
- More & smaller IMUs
- Sparse sensors

More comfortable

- Soft stretch sensors
- More & smaller IMUs
- Sparse sensors

Goal: Comfortable, Fast, and Accurate Motion Capture System

One sensor per segment.

Less sensor = Missing info

Infer through biomechanical constraints

Infer from additional measurements

Pred. | |

Meas. | |

Cstr. |

To make it work, we need to make assumptions that may not be practical for certain movements (e.g., Activities of Daily Living or ADLs).

Distance measurement which can be obtained through ultrasonic or ultra-wide band radio (UWB).

Remove pelvis related assumptions. Additional measurement model.

$$ \mathbf{H}(\mathbf{x}) = \tau_{k}^{pla} ( \hat{\theta}_{lk} ) \\

\tau_{k}^{pla} ( \theta_{lk} ) =
\overbrace{\tfrac{d^{p}}{2} \mathbf{r}^p_y - d^{ls} \mathbf{r}^{ls}_{z}}^{\psi, \text{ hip + shanks}}
+ \overbrace{d^{lt} \mathbf{r}_x^{ls} \sin{(\theta_{lk})}
-d^{lt} \mathbf{r}^{ls}_z \cos{(\theta_{lk})}
}^{\Lambda, \text{ thigh}} \\

(\hat{d}^{pla})^2 = \tau_{k}^{pla}(\theta_{lk})^2
= \psi^2 + 2 \psi \cdot \Lambda + (d^{lt})^2 \\

\alpha \cos{(\theta_{lk})} + \beta \sin{(\theta_{lk})} = \gamma \\

\alpha = - 2 d^{lt} \psi \cdot \mathbf{r}^{ls}_z, \quad
\beta = 2 d^{lt} \psi \cdot \mathbf{r}^{ls}_x \\

\gamma = (\hat{d}^{pla})^2 - \psi^2 - (d^{lt})^2 \\

\hat{\theta}_{lk} = \cos^{-1}\left( \tfrac{\alpha \gamma \pm \beta \sqrt{\alpha^2+\beta^2-\gamma^2}}{\alpha^2+\beta^2} \right)
$$

Tested on actual IMU data + simulated distance measurement from Sparse CKF dataset (walking, jumping jacks, speedskater, TUG, jog). Most deviation is at the turning motion ($t=3.5 - 5$s).

Dramatic increase in performance in dynamic movements. Captures Sagitall knee angles better.

Is able to locate relative position better. Note: TUG = Timed Up and Go.

Simulated at different levels of distance measurement noise $\sigma$ (assumed gaussian). Useful from $\sigma \leq 0.1$ m for walking. Useful from $\sigma \leq 0.2$ m for dynamic movements.

- Adding distance measurement is indeed a promising approach
- Can be implemented using ultrasound or Ultrawideband (UWB) based sensors.
- Simulated distance measurement needs actual validation.

- Code at https://git.io/JvLCF
- Interesting to try with better models and tracking more segments.

Infer from distance. Lie Group based 7 segment

Tested on actual IMU data + simulated distance measurement from Sparse CKF dataset (walking, jumping jacks, speedskater, TUG, jog).