Human movement analysis by means of accelerometers : aplication to human gait and motor symptoms of Parkinson's Disease
This thesis presents the original contributions of the author on the field of human movement analysis from signals captured by accelerometers. These sensors are capable of converting acceleration from some body parts into electric signals for further analysis. The progressive refinement and miniatur...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/134770 |
| Acceso en línea: | http://hdl.handle.net/10803/134770 https://dx.doi.org/10.5821/dissertation-2117-95248 |
| Access Level: | acceso abierto |
| Palabra clave: | 004 616.8 68 |
| Sumario: | This thesis presents the original contributions of the author on the field of human movement analysis from signals captured by accelerometers. These sensors are capable of converting acceleration from some body parts into electric signals for further analysis. The progressive refinement and miniaturization of accelerometers has allowed the development of minimally invasive devices that can be used to ambulatory monitor human movements during daily live activities. The study's contributions mainly fall under two heads: first, the analysis of movement in Parkinson's disease (PD); and, second, the relationship between accelerometer signals and characteristics of gait. To this end, new methods for obtaining speed and length of strides and, also, for identifying people have been developed. In all these studies, a single sensor fixed to the patient's waist has been used. PD is a neurodegenerative disease characterized by movement alterations. The main motor symptoms of PD are 1) tremor, 2) bradykinesia or slowness of movements, 3) freezing of gait and 4) dyskinesia or abnormal involuntary movements. The first three symptoms primarily occur when the medication has not yet reached an effective therapeutic effect. These periods are commonly known as OFF periods or OFF motor state. On the other hand, periods when the patient is suitably responding to the medication are known as ON periods or ON motor state. Dyskinesias mainly appear when the medication blood level is excessive. Both dyskinesias and OFF motor states are caused by a defect in the medication administration. In this sense, a wearable device capable of detecting and recording dyskinesias and OFF periods represents an important tool that enables clinics to more accurately prescribe the medication regimen of a patient. The work done in the field of PD consisted in developing algorithms able to detect dyskinesias and both ON and OFF periods. These algorithms have been adapted to provide real-time detection, which enabled their employment in a pilot study. This clinical study has tested, for the first time, the automatic adjustment of medication performed by means of a subcutaneous infusion pump according to the dyskinesias appearance and motor state of PD patients. The experience gained in the treatment of accelerometric signals from PD has led to contribute in the field of gait analysis. First, new methods for obtaining speed and length of strides from a single sensor fixed to the patient's waist have been obtained. Not only the PD can benefit from this study, but other diseases such as diabetes or some orthopedotraumatological diseases can also benefit from its results. Finally, using some of the techniques of the previous studies, another important contribution has been made in the field of biometric person identification. The work presented shows how the signal obtained from a single accelerometer located at the waist not only enables the extraction of some gait characteristics but also permits the identification of a person through its gait pattern. The main theoretical contribution of this thesis has been the development of techniques based on the reconstruction of attractors. It has been shown that the usage of only a small number of features that characterize the reconstructed attractor obtained from a time series of acceleration measurements makes possible the extraction of important parameters of gait and the person identification. |
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