Body Sensor Networking, Design and Algorithms. Saeid Sanei

Чтение книги онлайн.

Читать онлайн книгу Body Sensor Networking, Design and Algorithms - Saeid Sanei страница 22

Body Sensor Networking, Design and Algorithms - Saeid Sanei

Скачать книгу

Soda, P. and Iannello, G. (2006). A multi-expert system to classify fluorescent intensity in antinuclear autoantibodies testing. In: 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2006), 219–224. IEEE.

      24 24 Murray, E.D., Buttner, N., and Price, B.H. (2012). Depression and psychosis in neurological practice. In: Neurology in Clinical Practice, 6e (eds. W.G. Bradley, R.B. Daroff, G.M. Fenichel and J. Jankovic). Butterworth Heinemann.

      25 25 Sattar, H. (2011). Fundamentals of Pathology. Pathoma.

      26 26 Escudero, J., Sanei, S., Jarchi, D. et al. (2011). Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms. Physiological Measurements 32 (8): 1163–1180.

      27 27 National Institute of Neurological Disorders and Stroke (NINDS). (2016). Hydrocephalus Fact Sheet. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Hydrocephalus-Fact-Sheet (accessed 25 November 2019).

      28 28 Dayalu, P. and Albin, R.L. (2015). Huntington disease: pathogenesis and treatment. Neurologic Clinics 33 (1): 101–114.

      29 29 Caron, N.S., Wright, G.E.B., and Hayden, M.R. (1998). Huntington Disease. In: GeneReviews® (eds. M.P. Adam, H.H. Ardinger, R.A. Pagon, et al.). Seattle: University of Washington.

      30 30 Frank, S. (2014). Treatment of Huntington's disease. Neurotherapeutics 11 (1): 153–160.

      31 31 National Institute of Neurological Disorders and Stroke (NINDS). (2019). Huntington's Disease Information Page. https://www.ninds.nih.gov/Disorders/All-Disorders/Huntingtons-Disease-Information-Page (accessed 25 November 2019).

      32 32 World Health Organization (2020). Headache disorders. https://www.who.int/news-room/fact-sheets/detail/headache-disorders (accessed 1 January 2020).

      33 33 Aminoff, M.J., Greenberg, D.A., and Simon, R.P. (2009). Clinical Neurology, 7e. New York: Lange Medical Books/McGraw-Hill.

      34 34 Headache Classification Subcommittee of the International Headache Society (2004). The international classification of headache disorders: 2nd edition. Cephalalgia 24 (Suppl 1): 9–160.

      35 35 Pryse-Phillips, W. (2003). Companion to Clinical Neurology, 2e, 587. Oxford: Oxford University Press.

      36 36 Piane, M., Lulli, P., Farinelli, I. et al. (2007). Genetics of migraine and pharmacogenomics: some considerations. Journal of Headache and Pain 8 (6): 334–339.

      37 37 National Institute of Neurological Disorders and Stroke (NINDS). 2019. NINDS Multiple Sclerosis Information Page. https://www.ninds.nih.gov/Disorders/All-Disorders/Multiple-Sclerosis-Information-Page, (accessed 25 November 2019).

      38 38 Compston, A. and Coles, A. (2008). Multiple sclerosis. Lancet 372 (9648): 1502–1517.

      39 39 Compston, A. and Coles, A. (2002). Multiple sclerosis. Lancet 359 (9313): 1221–1231.

      40 40 Murray, E.D., Buttner, E.A., and Price, B.H. (2012). Depression and psychosis in neurological practice. In: Bradley's Neurology in Clinical Practice, 6e (eds. R. Daroff, G. Fenichel, J. Jankovic and J. Mazziotta). Philadelphia: Elsevier/Saunders.

      41 41 Kalia, L.V. and Lang, A.E. (2015). Parkinson's disease. Lancet 386 (9996): 896–912.

      42 42 National Institute of Neurological Disorders and Stroke (NINDS). (2016). Parkinson's Disease Information Page. https://www.ninds.nih.gov/Disorders/All-Disorders/Parkinsons-Disease-Information-Page (accessed 25 November 2019).

      43 43 Fisher, R., van Emde Boas, W., Blume, W. et al. (2005). Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46 (4): 470–472.

      44 44 Fisher, R.S., Acevedo, C., Arzimanoglou, A. et al. (2014). ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55 (4): 475–482.

      45 45 Taflinger, R. F., (1996) Taking ADvantage: The biological basis of human behavior, http://public.wsu.edu/∼taflinge/biology.html (accessed 25 November 2019).

      46 46 Marino, P. C. (2005) Biological rhythms as a basis for mood disorders, Http://www.personalityresearch.org/papers/marino.html (accessed 25 November 2019).

      47 47 Revelle, W., Humphreys, M.S., Simon, L., and Gilliland, K. (1980). The interactive effect of personality, time of day, and caffeine: a test of the arousal model. Journal of Experimental Psychology: General 109: 1–31.

      3.1 Introduction

      Nowadays, many of the physical, physiological, biological, and behavioural states of the human body can be measured, evaluated, and described by means of wearable sensors. These sensors can monitor the state of the human body for longer than an expert's observation. Often, the fusion of data modalities collected using different sensors is used for diagnostic purposes.

      Although the physical state of the human body can be observed in detail using video cameras or in some cases listened to using microphones, such modalities are subject to breach of privacy, costly to deploy, and are less fascinating for automated analysis body movement. Therefore, in this chapter we ignore these two modalities and investigate the cases where humans can wear sensors for a longer time to enable long-term monitoring. Here, the most popular methods for measuring very common human body states are explained and the advanced approaches described. The details as well as experimental considerations are described in later chapters.

      As described in Chapter 2, many physical or mental diseases or abnormalities directly or indirectly affect human gait. Stroke, Parkinson's, and leg amputation readily come to mind. Thus, gait analysis can be used to monitor both the cause and the symptoms of a wide range of such abnormalities. The state of gait can be measured using a number of sensing modalities (video, audio, footstep, acceleration, gravitational force, directionality, etc.). Among them, acceleration measurement is reasonably accurate, robust, cheap, and easy to do. It has been well established that in an unrestricted environment the most widely used method for effective gait analysis is performed using an accelerometer. This sensor is often combined with a gyro and magnetometer in a small and compatible inertial measurement unit (IMU).

Скачать книгу