A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors

With just the TINIEST BIT OF INITIATIVE any stroke clinician could run this same research using these sensors to come up with objective measurements on stroke muscular problems. And then create stroke protocols to fix those problems. This is so damned fucking easy, Why does it take a stroke addled survivor to even think of this ridiculously hard and difficult to implement research project? Do we have NO ONE in stroke that can rub two neurons together?  I should quit insulting our stroke medical professionals, it might hurt their fee fees. Oh well, I'm not running a popularity contest, I'm trying to solve stroke problems even if no one else is.
http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0149-2

• Federico LorussiEmail author,
• Nicola Carbonaro,
• Danilo De Rossi and
• Alessandro Tognetti

Journal of NeuroEngineering and Rehabilitation201613:40

DOI: 10.1186/s12984-016-0149-2

©  Lorussi et al. 2016

Received: 16 December 2015

Accepted: 14 April 2016

Published: 23 April 2016

Abstract

Background

Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors.

Method

To feed the reconstruction algorithm based on the bi-articular model, an ad-hoc sensing shirt was developed. The shirt was equipped with two inertial measurement units (IMUs) and an integrated textile strain sensor. We built the bi-articular model starting from the data obtained in two planar movements (arm abduction and flexion in the sagittal plane) and analysing the error between the reference data - measured through an optical reference system - and the socket-ball approximation of the shoulder. The 3D model was developed by extending the behaviour of the kinematic chain revealed in the planar trajectories through a parameter identification that takes into account the body structure of the subject.

Result

The bi-articular model was evaluated in five subjects in comparison with the optical reference system. The errors were computed in terms of distance between the reference position of the trochlea (end-effector) and the correspondent model estimation. The introduced method remarkably improved the estimation of the position of the trochlea (and consequently the estimation of the hand position during reaching activities) reducing position errors from 11.5 cm to 1.8 cm.

Conclusion

Thanks to the developed bi-articular model, we demonstrated a reliable estimation of the upper arm kinematics with a minimal sensing system suitable for daily life monitoring of recovery.