Gerenciamento e armazenamento de dados de sensores para a avaliação da doença de Parkinson: Uma revisão
Resumo
O aumento da expectativa de vida da população levou, entre outros aspectos, ao crescimento da ocorrência de doenças neurodegenerativas, como a doença de Parkinson (DP). Nesse sentido, com o objetivo de melhorar a qualidade de vida da população, os serviços remotos de monitoramento de saúde habilitados com sensores estão se tornando mais comuns. No entanto, ainda existem desafios quanto ao gerenciamento e armazenamento das grandes quantidades dos dados coletados por esses sensores. A fim de abordar soluções para os desafios citados anteriormente, neste artigo é feita um mapeamento sistemático de literatura. Através dos resultados obtidos, observou-se uma baixa quantidade de artigos com detalhes específicos sobre o tema.
Palavras-chave:
Expectativa de vida, doenças, Parkinson, monitoramento, sistema
Referências
Blount, M., Batra, V. M., Capella, A. N., et al. (2010). Remote health-care monitoring using Personal Care Connect. IBM Systems Journal, v. 46, n. 1, p. 95–113.
C. F. Gomes, T., C. Oliveira, E., C. Oliveira, L., et al. (2018). Aplicação de Realidade Virtual para Análise e Reabilitação de Pacientes com Doença de Parkinson.
Ciuti, G., Ricotti, L., Menciassi, A. and Dario, P. (2015). MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: A review on research activities in Italy. Sensors (Switzerland), v. 15, n. 3, p. 6441–6468.
Cooper, J., James, A. and Cooper Hildebrand, J. (2009). Challenges for Database Management in the Internet of Things Cooper J, James A - IETE Tech Rev. IETE Technical Review, v. 26, n. 5, p. 320–329.
Dickson, J. M. and Grünewald, R. A. (2004). Somatic symptom progression in idiopathic Parkinson’s disease. Parkinsonism and Related Disorders, v. 10, n. 8, p. 487–492.
Espay, A. J., Bonato, P., Nahab, F. B., et al. (2016). Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, v. 31, n. 9, p. 1272–1282.
Farahani, B., Firouzi, F., Chang, V., et al. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, v. 78, p. 659–676.
Goetz, C. G., Fahn, S., Martinez-Martin, P., et al. (2007). Movement disorder society- sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): Process, format, and clinimetric testing plan. Movement Disorders, v. 22, n. 1, p. 41–47.
Hallett, M. (2008). Overview of Human Tremor Physiology. Movement Disorders, v. 13, n. S3, p. 43–48.
Han, J., Kamber, M. and Pei, J. (2012). Data Preprocessing. Data Mining. Elsevier. p. 83–124.
Jordao, A., Nazare, A. C., Sena, J. and Schwartz, W. R. (2018). Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art. p. 1–11.
Milosevic, B. and Farella, E. (2017). Wireless MEMS for wearable sensor networks. Wireless MEMS Networks and Applications. Elsevier. p. 101–127.
Mohammadzadeh, N. and Safdari, R. (2018). Patient Monitoring in Mobile Health : Opportunities and Challenges Background : Methods : Results : Conclusions : v. 68, n. 1, p. 57–60.
Nguyen, H. H., Mirza, F., Naeem, M. A. and Nguyen, M. (2017). A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real- time clinical feedback. Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design, CSCWD 2017, n. April, p. 257–262.
Nweke, H. F., Teh, Y. W., Mujtaba, G. and Al-garadi, M. A. (2019). Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion, v. 46, n. June 2018, p. 147–170.
Perumal, S. V. and Sankar, R. (2016). Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. ICT Express, v. 2, n. 4, p. 168–174.
Rossi, B., Siciliano, G., Carboncini, M. C., et al. (1996). Muscle modifications in Parkinson’s disease: Myoelectric manifestations. Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control, v. 101, n. 3, p. 211–218.
Teshuva, I., Hillel, I., Gazit, E., et al. (2019). Using wearables to assess bradykinesia and rigidity in patients with Parkinson’s disease: a focused, narrative review of the literature. Journal of Neural Transmission, v. 126, n. 6, p. 699–710.
Yu, J., Kim, M., Bang, H. C., Bae, S. H. and Kim, S. J. (2016). IoT as a applications: cloud-based building management systems for the internet of things. Multimedia Tools and Applications, v. 75, n. 22, p. 14583–14596.
C. F. Gomes, T., C. Oliveira, E., C. Oliveira, L., et al. (2018). Aplicação de Realidade Virtual para Análise e Reabilitação de Pacientes com Doença de Parkinson.
Ciuti, G., Ricotti, L., Menciassi, A. and Dario, P. (2015). MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: A review on research activities in Italy. Sensors (Switzerland), v. 15, n. 3, p. 6441–6468.
Cooper, J., James, A. and Cooper Hildebrand, J. (2009). Challenges for Database Management in the Internet of Things Cooper J, James A - IETE Tech Rev. IETE Technical Review, v. 26, n. 5, p. 320–329.
Dickson, J. M. and Grünewald, R. A. (2004). Somatic symptom progression in idiopathic Parkinson’s disease. Parkinsonism and Related Disorders, v. 10, n. 8, p. 487–492.
Espay, A. J., Bonato, P., Nahab, F. B., et al. (2016). Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, v. 31, n. 9, p. 1272–1282.
Farahani, B., Firouzi, F., Chang, V., et al. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, v. 78, p. 659–676.
Goetz, C. G., Fahn, S., Martinez-Martin, P., et al. (2007). Movement disorder society- sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): Process, format, and clinimetric testing plan. Movement Disorders, v. 22, n. 1, p. 41–47.
Hallett, M. (2008). Overview of Human Tremor Physiology. Movement Disorders, v. 13, n. S3, p. 43–48.
Han, J., Kamber, M. and Pei, J. (2012). Data Preprocessing. Data Mining. Elsevier. p. 83–124.
Jordao, A., Nazare, A. C., Sena, J. and Schwartz, W. R. (2018). Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art. p. 1–11.
Milosevic, B. and Farella, E. (2017). Wireless MEMS for wearable sensor networks. Wireless MEMS Networks and Applications. Elsevier. p. 101–127.
Mohammadzadeh, N. and Safdari, R. (2018). Patient Monitoring in Mobile Health : Opportunities and Challenges Background : Methods : Results : Conclusions : v. 68, n. 1, p. 57–60.
Nguyen, H. H., Mirza, F., Naeem, M. A. and Nguyen, M. (2017). A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real- time clinical feedback. Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design, CSCWD 2017, n. April, p. 257–262.
Nweke, H. F., Teh, Y. W., Mujtaba, G. and Al-garadi, M. A. (2019). Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion, v. 46, n. June 2018, p. 147–170.
Perumal, S. V. and Sankar, R. (2016). Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. ICT Express, v. 2, n. 4, p. 168–174.
Rossi, B., Siciliano, G., Carboncini, M. C., et al. (1996). Muscle modifications in Parkinson’s disease: Myoelectric manifestations. Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control, v. 101, n. 3, p. 211–218.
Teshuva, I., Hillel, I., Gazit, E., et al. (2019). Using wearables to assess bradykinesia and rigidity in patients with Parkinson’s disease: a focused, narrative review of the literature. Journal of Neural Transmission, v. 126, n. 6, p. 699–710.
Yu, J., Kim, M., Bang, H. C., Bae, S. H. and Kim, S. J. (2016). IoT as a applications: cloud-based building management systems for the internet of things. Multimedia Tools and Applications, v. 75, n. 22, p. 14583–14596.
Publicado
22/11/2019
Como Citar
CANDEIRA, Daniel B.; OLIVEIRA, Fábio Henrique M. .
Gerenciamento e armazenamento de dados de sensores para a avaliação da doença de Parkinson: Uma revisão. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 7. , 2019, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 303-311.
