Analysis of time series of surface electromyography and accelerometry in dogs

Author(s)

Negrao, Roberta Rocha ; Rahal, Sheila Canevese ; Kano, Washington Takashi ; Mesquita, Luciane Reis ; Hormaza, Joel Mesa

• Document type: Journal article
• Source: Biomedical Signal Processing and Control; v. 74, p. 6-pg., 2022-04-01.

Abstract

Accelerometers may be used as a tool for objective analysis of kinetic and/or temporospatial gait parameters; however, only a few studies have been done in small animals. Therefore, this study analysed surface electromyography (sEMG) signals related to inertial sensors (accelerometry), aiming to create a reproducible standard in dogs. The hypothesis was that the combined use of an accelerometer and sEMG sensors could be used to identify the phases of the gait cycle and that the standard data established in healthy dogs could be used to compare data obtained from dogs with hip dysplasia. These signs were obtained from two different muscles, the biceps femoris and vastus lateralis muscles, from two breeds of dogs (Labrador retriever and golden retriever), during a walking gait at a controlled velocity. After signal processing, a second-order low-pass Butterworth filter with a cut-off frequency of 6 Hz was used, and an algorithm was applied to determine a threshold value for the gait cycle phases. Then, correlations between signals from both transducers were determined. Data relating to percent muscle activity, correlation and asymmetric functions, stance time and swing during the gait cycle in healthy dogs were generated after signal processing. Signals collected from dogs with hip dysplasia fell outside of the reference intervals established in healthy dogs. In conclusion, the methods applied for signal analysis and processing allowed the identification of structures of muscular activity during the gait cycle and the establishment of normal distribution values in healthy dogs. (AU)

• FAPESP's process: 14/23755-0 - Influence of the gait type on the electromyographic pattern
• Grantee: Sheila Canevese Rahal
• Support Opportunities: Regular Research Grants