Shoulder pain and jerk during recovery phase of manual wheelchair propulsion.

UNLABELLED: Repetitive loading of the upper limb due to wheelchair propulsion plays a leading role in the development of shoulder pain in manual wheelchair users (mWCUs). There has been minimal inquiry on understanding wheelchair propulsion kinematics from a human movement ergonomics perspective. This investigation employs an ergonomic metric, jerk, to characterize the recovery phase kinematics of two recommended manual wheelchair propulsion patterns: semi-circular and the double loop. Further it examines if jerk is related to shoulder pain in mWCUs. Data from 22 experienced adult mWCUs was analyzed for this study (semi-circular: n=12 (pain/without-pain:6/6); double-loop: n=10 (pain/without-pain:4/6)). Participants propelled their own wheelchair fitted with SMARTWheels on a roller dynamometer at 1.1 m/s for 3 min. Kinematic and kinetic data of the upper limbs were recorded. Three dimensional absolute jerk experienced at the shoulder, elbow and wrist joint during the recovery phase of wheelchair propulsion were computed. Two-way ANOVAs were conducted with the recovery pattern type and shoulder pain as between group factors. FINDINGS: (1) Individuals using a semi-circular pattern experienced lower jerk at their arm joints than those using a double loop pattern (P<0.05, η(2)=0.32)wrist;(P=0.05, η(2)=0.19)elbow;(P<0.05, η(2)=0.34)shoulder and (2) individuals with shoulder pain had lower peak jerk magnitude during the recovery phase (P≤0.05, η(2)=0.36)wrist;(P≤0.05, η(2)=0.30)elbow;(P≤0.05, η(2)=0.31)shoulder. CONCLUSIONS: Jerk during wheelchair propulsion was able to distinguish between pattern types (semi-circular and double loop) and the presence of shoulder pain. Jerk provides novel insights into wheelchair propulsion kinematics and in the future it may be beneficial to incorporate jerk based metric into rehabilitation practice.

Shoulder pain and cycle to cycle kinematic spatial variability during recovery phase in manual wheelchair users: a pilot investigation.

UNLABELLED: Wheelchair propulsion plays a significant role in the development of shoulder pain in manual wheelchair users (MWU). However wheelchair propulsion metrics related to shoulder pain are not clearly understood. This investigation examined intra-individual kinematic spatial variability during semi-circular wheelchair propulsion as a function of shoulder pain in MWU. Data from 10 experienced adult MWU with spinal cord injury (5 with shoulder pain; 5 without shoulder pain) were analyzed in this study. Participants propelled their own wheelchairs on a dynamometer at 3 distinct speeds (self-selected, 0.7 m/s, 1.1 m/s) for 3 minutes at each speed. Motion capture data of the upper limbs were recorded. Intra-individual kinematic spatial variability of the steady state wrist motion during the recovery phase was determined using principal component analysis (PCA). The kinematic spatial variability was calculated at every 10% intervals (i.e at 11 interval points, from 0% to 100%) along the wrist recovery path. RESULTS: Overall, spatial variability was found to be highest at the start and end of the recovery phase and lowest during the middle of the recovery path. Individuals with shoulder pain displayed significantly higher kinematic spatial variability than individuals without shoulder pain at the start (at 10% interval) of the recovery phase (p<.004). CONCLUSIONS: Analysis of intra-individual kinematic spatial variability during the recovery phase of manual wheelchair propulsion distinguished between those with and without shoulder pain. Variability analysis of wheelchair propulsion may offer a new approach to monitor the development and rehabilitation of shoulder pain.

On the use of multivariable piecewise-linear models for predicting human response to anesthesia.

The standard modeling paradigm used to describe the relationship between input anesthetic agents and output patient endpoint variables are single-input single-output pharmacokinetic-pharmacodynamic (PK-PD) compartment models. In this paper, we propose the use of multivariable piecewise-linear models to describe the relations between inputs that include anesthesia, surgical stimuli and disturbances to a variety of patient output variables. Subspace identification methods are applied to clinical data to construct the models. A comparison of predicted and measured responses is completed, which includes predictions from PK-PD models, and piecewise-linear time-invariant models.