Modeling Muscle Synergies as a Gaussian Process: Estimating Unmeasured Muscle Excitations using a Measured Subset.

Continuous observation of muscle activity could provide a comprehensive picture of the loads experienced by muscles and joints during daily life. However, a major limitation to the practical application of this approach is the need to have surface electromyography (sEMG) sensors on all involved muscles. In this work, we model the synergistic relationship between muscles as a Gaussian process enabling the inference of unmeasured muscle excitations using a subset of measured data. Specifically, we developed a model for a single subject which uses sEMG data from four leg muscles to estimate the muscle excitation time-series of six other leg muscles during level walking at a self-selected speed. The proposed technique was able to accurately estimate the held-out muscle excitation time-series of the six muscles with correlation coefficients ranging from 0.74 to 0.87 and with mean absolute error less than 3%.

A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset.

Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life.

Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles.

Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12 . 32 ∘ and 11 . 82 ∘ vs. 24 . 61 ∘ and 23 . 76 ∘ ) and flexion/extension joint angles ( 7 . 88 ∘ and 8 . 62 ∘ vs. 14 . 14 ∘ and 15 . 64 ∘ ). Also, ROM estimation error was less than 2 . 2 ∘ during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.