A wireless, modular and wearable system for the recognition and assessment of foot drop pathology
In this paper, a portable, low cost and non-invasive real-time signals processing prototype was designed and developed for the diagnosis and continuous monitoring of the physiopathological condition of foot drop. The behavior of the electrical activity of the Tibialis Anterior (TA) and Peroneus Long...
- Autores:
-
Noriega Alvarez, Santiago
Rojas, Maria C.
Murrugarra Quiroz, Cecilia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad El Bosque
- Repositorio:
- Repositorio U. El Bosque
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/4673
- Acceso en línea:
- http://hdl.handle.net/20.500.12495/4673
https://doi.org/10.1007/978-3-030-31019-6_33
- Palabra clave:
- Foot drop
Rehabilitation
EMG
Sensors
Real time, signal acquisition
- Rights
- openAccess
- License
- Acceso abierto
| Summary: | In this paper, a portable, low cost and non-invasive real-time signals processing prototype was designed and developed for the diagnosis and continuous monitoring of the physiopathological condition of foot drop. The behavior of the electrical activity of the Tibialis Anterior (TA) and Peroneus Longus (PL) muscles through bipolar surface electromyography (sEMG), together with the angular measurement of the joint complex of the ankle-foot in the sagittal and frontal planes using an Inertial Measurement Unit (IMU) sensor system, are monitored from a mobile interface. This prototype consists of five modules capable of performing functions of sensing, signal processing, data storage, and transmission. The Central Processing Unit (CPU) process the sEMG signals from the two-channel amplifier with 10 bits of resolution at a sampling frequency of 1ksps; the IMU Sensor System operates at a sample rate of 1ksps with 16 bits of resolution. Both sEMG and angular displacement data registers are transmitted wirelessly via Bluetooth communication protocol to a mobile interface designed for smartphones/tablets and PC. Data verification was made using a commercial electromyograph and a goniometer. The observations regarding the health status of the patient on a statistical, mathematical analysis of the collected data, exhibiting a mean-square-error of 5,27% for the sEMG as well as an average error of ≤±2∘ in the angular displacement measurements. The prototype designed and developed establishes a new perspective in the recognition and elaboration of profiles of physiopathological disabilities in humans, development of clinical applications, and databases for future studies of the disease. |
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