Body Sensor Networking, Design and Algorithms. Saeid Sanei
Чтение книги онлайн.
Читать онлайн книгу Body Sensor Networking, Design and Algorithms - Saeid Sanei страница 21
Название: Body Sensor Networking, Design and Algorithms
Автор: Saeid Sanei
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119390015
isbn:
There are many factors influencing biological rhythms. A cluster of approximately 10 000 nerve cells located on the suprachiasmatic nuclei (SCN) found on the hypothalamus in the brain. The circadian clock's primary function is to interpret external changes of light and darkness, as well as social contact, in order to establish diurnal rhythms. It is not uncommon for the circadian clock to be disrupted temporarily; events such as changes in work schedule from day to night, changing time zones, and to some extent old age can impact the consistency of circadian rhythms.
The circadian clock relies heavily on changes in light to determine day/night transitions. During the night, SCN emits melatonin hormone, which induces sleep. The process of wake to sleep itself has its own stages and each stage has its own duration [17].
Another major disruptive factor related to the circadian clock's interpretation of light is seasonal change. During the winter months, there are fewer daylight hours. As a result, the level of melatonin secretion increases along with the number of hours of darkness. The normal cycles may also be interrupted by changing one's daily habits, for example changing feeding time, following a gradual force-to-sleep or sleep depriving.
In addition to these major influences there are a variety of other environmental factors that may have an impact on biological rhythms. One of them is caffeine. A series of experiments on caffeine revealed differences in the effects of the drug depending on time of day. In the morning caffeine has been shown to hinder low impulsiveness, while the opposite is true in the evening [47]. This finding suggests that low impulsiveness and high impulsiveness differ in the phase of their diurnal rhythms, resulting in a difference in the effects of caffeine.
By establishing an understanding of various environmental factors that influence biological rhythms it is possible to draw connections between the significant time shifts and changes in nature and mood disorders.
Moreover, the influence of those factors may be quantified by developing a hybrid measurement system incorporating measures of brain activity, heart rate, and respiration as well as changes in the level of adrenalin in the blood over time.
2.6 Summary and Conclusions
Physical, biological, and mental biomarkers of the human body can well describe its state. Body movement, heart rate variability, and the brain responses to various internal and external stimuli can reveal the symptoms and causes of many abnormalities in the state of human body. To differentiate these abnormalities and disease indicators, however, a variety of tests and measurements by means of suitable sensors need to be undertaken. These indicators can be quantified using data-processing and intelligent systems for better and quicker diagnosis of the abnormalities in humans. Although sensors and sensory networks have facilitated recording and quantification of many of the states indicating variables, there is still a long way to go to cover all factors involved in the full recognition of human body states.
References
1 1 Lee, T.K.M., Belkhatir, M., and Sanei, S. (2014). A comprehensive review of past and present vision-based techniques for gait recognition. Multimedia Tools and Applications 72 (3): 2833–2869.
2 2 Lee, T.K.M., Belkhatir, M., Lee, P.A., and Sanei, S. (2008). Nonlinear characterisation of fronto-normal gait for human recognition. In: Advances in Multimedia Information Processing – PCM 2008, Lecture Notes in Computer Science (eds. Y.-M.R. Huang et al.), 466–475. Berlin: Springer-Verlag.
3 3 Caldas, R., Mundt, M., Potthast, W., Buarque de Lima Neto, F., and Markert, B. (2017) A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms. Gait Posture 57: 204–210.
4 4 Jarchi, D., Wong, C., Kwasnicki, R.M. et al. (2014). Gait parameter estimation from a miniaturised ear-worn sensor using singular spectrum analysis and longest common subsequence. IEEE Transactions on Biomedical Engineering 61 (4): 1261–1273.
5 5 Kumar, P., Mukherjee, S., Saimi, R. et al. (2019). Multimodal gait recognition with inertial sensor data and video using evolutionary algorithm. IEEE Transactions on Fuzzy Systems 27 (5): 956–965.
6 6 Zhou, X. and Bhanu, B. (2006). Feature fusion of face and gait for human recognition at a distance in video. Proceedings of the 18th International Conference on Pattern Recognition 4: 529–532.
7 7 Bazin, A. I. (2006) On probabilistic methods for object description and classification. PhD thesis, University of Southampton.
8 8 Andrews, K. and Steward, J. (1978). Stroke recovery: he can but does he? Rheumatology 16 (1): 43–48.
9 9 Lee, T. K. M., Gan, S.S.W., Sanei, S., and Kouchaki, S., (2013) Assessing rehabilitative reach and grasp movements with singular spectrum analysis. Proceedings of the 21st European Signal Processing Conference (EUSIPCO), Marrakech, Morocco (9–13 September 2013).
10 10 Jenkins, K.J., Correa, A., Feinstein, J.A. et al. (2007). Noninherited risk factors and congenital cardiovascular defects: current knowledge: American Heart Association Council on cardiovascular disease in the young: endorsed by the American Academy of Pediatrics. Circulation 115 (23): 2995–3014.
11 11 Mendis, S. and Puska, P. (2011). Global Atlas on Cardiovascular Disease Prevention and Control (ed. World Health Organization), 3. World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization.
12 12 Shelat, A. M., (2016) Electromyography, https://medlineplus.gov/ency/article/003929.htm (accessed 25 November 2019).
13 13 Longo, G. and Montévil, M. (2014). Perspectives on Organisms. Springer.
14 14 Bu, Z. and Callaway, D.J. (2011). Proteins MOVE! Protein dynamics and long-range allostery in cell signalling. Advances in Protein Chemistry and Structural Biology 83: 163–221.
15 15 Cotsapas, C. and Haer, D.A. (2013). Immune-mediated disease genetics: the shared basis of pathogenesis. Trends in Immunology 34: 22–26.
16 16 Meroni, P.L. and Schur, P.H. (2010). Ana screening: an old test with new recommendations. Annals of the Rheumatic Diseases 69: 1420–1422.
17 17 Sanei, S. (2013). Adaptive Processing of Brain Signals. Wiley.
18 18 Hiemann, R., Hilger, N., Sack, U., and Weigert, M. (2006). Objective quality evaluation of fluorescence images to optimize automatic image acquisition. Cytometry Part A 69: 182–184.
19 19 Soda, P., Rigon, A., Afeltra, A., and Iannello, G. (2006). Automatic acquisition of immunofluorescence images: algorithms and evaluation. In: 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS, 386–390. IEEE.
20 20 Huang, Y.-L., Chung, C.-W., Hsieh, T.-Y., and Jao, Y.-L. (2008). Outline detection for the HEp-2 cell in indirect immunofluorescence images using watershed segmentation. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing (SUTC'08), 423–427. IEEE.
21 21 Huang, Y.-L., Jao, Y.-L., Hsieh, T.-Y., and Chung, C.-W. (2008). Adaptive automatic segmentation of HEp-2 cells in indirect immunofluorescence images. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing (SUTC'08), 418–422. IEEE.
22 22 Foggia, P., Percannella, G., Soda, P., and Vento, M. (2010). Early experiences in mitotic cells recognition on HEp-2 slides. In: IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS), 38–43. IEEE.
23 23 СКАЧАТЬ