Predição de Congelamento de Marcha por Meio da Detecção de Eventos de Pré-Congelamento

• João Pedro A. Amoedo UFAM
• Paulo H. N. Gonçalves UFAM
• Rafael Albuquerque UFAM
• Eulanda M. dos Santos UFAM
• Rafael Giusti UFAM
• Renato C. F. Junior UFAM

DOI: https://doi.org/10.5753/sbcas.2023.229854

Resumo

Congelamento de marcha (FOG) é um sinal clínico debilitante comumente observado em pacientes com doença de Parkinson. Utilizando dados normalmente capturados por sensores inerciais, a predição automática do FOG pode ajudar a melhorar a qualidade de vida dos pacientes—e.g., evitando quedas. Dentre as estratégias aplicadas, destaca-se o uso de métodos de aprendizagem de máquina para resolver um problema de classificação em três classes: FOG, Não-FOG e Pré-FOG. A classe Pré-FOG refere-se a segmentos de série temporal imediatamente anterior à ocorrência de um evento de FOG. A expectativa é que instâncias de Pré-FOG apresentem características únicas que permitam identificar aspectos de um episódio iminente de FOG. No entanto, essa classe não é bem definida e nem bem caracterizada na literatura devido à sua natureza transicional. Neste artigo nós analisamos o comportamento da classe Pré-FOG usando dois modelos de aprendizagem de máquina, SVM e a combinação de CNN com LSTM, em dados coletados por acelerômetros e giroscópios. Os resultados mostram que Pré-FOG pode ser detectada, mas com taxas de reconhecimento baixas, indicando a falta de padrões discriminantes da classe Pré-FOG em relação às instâncias de FOG ou de Não-FOG.

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