Temporal Synergies Detection in Gait Cyclograms Using Wearable Technology.

The human gait can be described as the synergistic activity of all individual components of the sensory-motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures.

A principal component analysis (PCA) based assessment of the gait performance.

The gait assessment is instrumental for evaluating the efficiency of rehabilitation of persons with a motor impairment of the lower extremities. The protocol for quantifying the gait performance needs to be simple and easy to implement; therefore, a wearable system and user-friendly computer program are preferable. We used the Gait Master (instrumented insoles) with the industrial quality ground reaction forces (GRF) sensors and 6D inertial measurement units (IMU). WiFi transmitted 10 signals from the GRF sensors and 12 signals from the accelerometers and gyroscopes to the host computer. The clinician was following in real-time the acquired data to be assured that the WiFi operated correctly. We developed a method that uses principal component analysis (PCA) to provide a clinician with easy to interpret cyclograms showing the difference between the recorded and healthy-like gait performance. The cyclograms formed by the first two principal components in the PCA space show the step-to-step reproducibility. We suggest that a cyclogram and its orientation to the coordinate system PC1 vs. PC2 allow a simple assessment of the gait. We show results for six healthy persons and five patients with hemiplegia.