Biomechanics of wheelchair turning manoeuvres: novel insights into wheelchair propulsion.

Introduction: Wheelchair turning biomechanics is an under researched area despite its obvious relevance to functional mobility of wheelchair users. Wheelchair turns might be linked to a higher risk of upper limb injuries due to the increased forces and torques potentially associated with asymmetric movement. Our aim was to obtain a better theoretical understanding of wheelchair turning by biomechanically analyzing turns compared to steady-state straightforward propulsion (SSSFP). Methods: Ten able-bodied men received 12-min familiarization and 10 trials (in a random order) of SSSFP and multiple left and right turns around a rectangular course. A Smartwheel was mounted at the right wheel of a standard wheelchair to measure kinetic parameters during SSSFP and of the inner hand during right turns and the outer hand during left turns. A repeated measures ANOVA was used to detect differences across tasks. Results: Two strategies were identified: 3% demonstrated roll turns and 97% spin turns. Spin turns consisted of three phases: approach, turning and depart phase. The turning phase was accomplished by increasing peak force (72.9 ± 25.1 N vs. 43.38 ± 15.9 N in SSSFP) of the inner hand, while maintaining high push frequency of the outer hand (1.09 ± 0.20 push/s vs. 0.95 ± 0.13 push/s in SSSFP). Peak negative force and force impulse during the turning phase were much higher than SSSFP, 15.3 ± 15.7 and 4.5 ± 1.7 times higher, respectively. Conclusion: The spin turn strategy might carry an increased risk of upper limb injuries due to higher braking force and requires particular attention by rehabilitation professionals to preserve upper limb function of long-term wheelchair users.

Can shoulder joint reaction forces be estimated by neural networks?

To facilitate the development of future shoulder endoprostheses, a long term load profile of the shoulder joint is desired. A musculoskeletal model using 3D kinematics and external forces as input can estimate the mechanical load on the glenohumeral joint, in terms of joint reaction forces. For long term ambulatory measurements, these 3D kinematics can be measured by means of Inertial Magnetic Measurement Systems. Recording of external forces under daily conditions is not feasible; estimations of joint loading should preferably be independent of this input. EMG signals reflect the musculoskeletal response and can easily be measured under daily conditions. This study presents the use of a neural network for the prediction of glenohumeral joint reaction forces based upon arm kinematics and shoulder muscle EMG. Several setups were examined for NN training, with varying combinations of type of input, type of motion, and handled weights. When joint reaction forces are predicted by a trained NN, for motion data independent of the training data, results show a high intraclass correlation (ICC up to 0.98) and relative SEM as low as 3%, compared to similar output of a musculoskeletal model. A convenient setup in which kinematics and only one channel of EMG were used as input for the NN׳s showed comparable predictive power as more complex setups. These results are promising and enable long term estimation of shoulder joint reaction forces outside the motion lab, independent of external forces.

Determining a long term ambulatory load profile of the shoulder joint: neural networks predicting input for a musculoskeletal model.

To gain more insight in the development of joint damage, a long term load profile of the shoulder joint under daily living conditions is desirable. Standard musculoskeletal models estimate joint load using kinematics and exerted force. However, the latter cannot be measured continuously in ambulatory settings, hampering the use of these models. This paper describes a method for obtaining such a load profile, by training a Neural Network (NN), using kinematics and EMG. A small data set of specified movements with known exerted forces is used in two ways. First, the model calculates several variables of joint load, and a set of Generalized Forces and Net Moments (GFNM) around the model's degrees of freedom. Second, using kinematics and EMG, an NN is trained to predict these GFNM, which can concurrently be used as input for the model, resulting in full model output independent of exerted force. The method is validated with an independent trial. The NN could predict GFNM within 10% relative RMS, compared to output of the model. The NN-model combination estimated joint reaction forces with relative RMS values of 7 to 17%, enabling the estimation of a detailed load profile of the shoulder under daily conditions.

Functionally interpretable local coordinate systems for the upper extremity using inertial & magnetic measurement systems.

BACKGROUND: Inertial Magnetic Measurement Systems (IMMS) are becoming increasingly popular by allowing for measurements outside the motion laboratory. The latest models enable long term, accurate measurement of segment motion in terms of joint angles, if initial segment orientations can accurately be determined. The standard procedure for definition of segmental orientation is based on the measurement of positions of bony landmarks (BLM). However, IMMS do not deliver position information, so an alternative method to establish IMMS based, anatomically understandable segment orientations is proposed. METHODS: For five subjects, IMMS recordings were collected in a standard anatomical position for definition of static axes, and during a series of standardized motions for the estimation of kinematic axes of rotation. For all axes, the intra- and inter-individual dispersion was estimated. Subsequently, local coordinate systems (LCS) were constructed on the basis of the combination of IMMS axes with the lowest dispersion and compared with BLM based LCS. FINDINGS: The repeatability of the method appeared to be high; for every segment at least two axes could be determined with a dispersion of at most 3.8 degrees. Comparison of IMMS based with BLM based LCS yielded compatible results for the thorax, but less compatible results for the humerus, forearm and hand, where differences in orientation rose to 17.2 degrees. INTERPRETATION: Although different from the 'gold standard' BLM based LCS, IMMS based LCS can be constructed repeatable, enabling the estimation of segment orientations outside the laboratory. CONCLUSIONS: A procedure for the definition of local reference frames using IMMS is proposed.

Magnetic distortion in motion labs, implications for validating inertial magnetic sensors.

BACKGROUND: Ambulatory 3D orientation estimation with Inertial Magnetic Sensor Units (IMU's) use the earth magnetic field. The magnitude of distortion in orientation in a standard equipped motion lab and its effect on the accuracy of the orientation estimation with IMU's is addressed. METHODS: Orientations of the earth magnetic field vectors were expressed in the laboratory's reference frame. The effect of a distorted earth magnetic field on orientation estimation with IMU's (using both a quaternion and a Kalman fusing algorithm) was compared to orientations derived from an optical system. FINDINGS: The magnetic field varied considerably, with the strongest effects at 5 cm above floor level with a standard deviation in heading of 29 degrees , decreasing to 3 degrees at levels higher than 100 cm. Orientation estimation was poor with the quaternion filter, for the Kalman filter results were acceptable, despite a systematic deterioration over time (after 20-30s). INTERPRETATION: Distortion of the earth magnetic field is depending on construction materials used in the building, and should be taken into account for calibration, alignment to a reference system, and further measurements. Mapping the measurement volume to determine its ferromagnetic characteristics in advance of planned experiments can be the rescue of the data set. CONCLUSIONS: To obtain valid data, "mapping" of the laboratory is essential, although less critical with the Kalman filter and at larger distances (>100 cm) from suspect materials. Measurements should start in a "safe" area and continue no longer than 20-30s in a heavily distorted earth magnetic field.