Body Sensor Networking, Design and Algorithms - Saeid Sanei

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Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA (3–5 April 2006). IEEE https://doi.org/10.1109/BSN.2006.5.

      48 48 Sanei, S. and Hassani, H. (2015). Singular Spectrum Analysis of Biomedical Signals. CRC Press.

      49 49 Sanei, S. (2013). Adaptive Processing of Brain Signals. Wiley.

      50 50 Natarajan, A., Motani, M., de Silva, B. et al. (2007). Investigating network architectures for body sensor networks. In: Proceedings of the 1st ACM SIGMOBILE International Workshop on Systems and Networking Support For Healthcare and Assisted Living Environments (HealthNet 2007), San Juan, Puerto Rico (June 2007), 19–24. ACM.

      51 51 Tachtatzis, C., Graham, B., Tracey, D. et al. (2011). On-body to on-body channel characterization. In: Proceedings of 2011 IEEE Sensors Conference, Limerick, Ireland (28–31 October 2011), 908–911. IEEE.

      52 52 Ullah, S., Shen, B., Riazul, I.S. et al. (2010). A study of MAC protocols for WBANs. Sensors (Basel) 10: 128–145.

      53 53 Seo, S.H., Gopalan, S.A., Chun, S.M. et al. (2010). An energy-efficient configuration management for multi-hop wireless body area networks. In: Proceedings of 3rd IEEE International Conference on Broadband Network and Multimedia Technology, Beijing, China (26–28 October 2010), 1235–1239. IEEE.

      54 54 Benoît, L., Eli, D.P., Ingrid, M., and Piet, D. (2007). MOFBAN: a lightweight modular framework for body area networks. Lecture Notes in Computer Science 4808: 610–622.

      55 55 Toorani, M. (2015). On vulnerabilities of the security association in the IEEE 802.15.6 Standard. In: Financial Cryptography and Data Security (eds. M. Brenner, N. Christin, B. Johnson and K. Rohloff). Berlin: Springer.

      56 56 O'Donoghue, J., Herbert, J. and Fensli, R., (2006) Sensor validation within a pervasive medical environment, Proceedings of the 5th IEEE Conference on Sensors, Daegu, South Korea (22–25 October 2006), 972–975.

      57 57 O'Donoghue, J., Herbert, J., and Kennedy, R. (2006). Data consistency within a pervasive medical environment. In: Proceedings of IEEE Sensors. IEEE.

      58 58 Garcia, P., Virginia Pilloni, V., Franceschelli, F. et al. (2018). Deployment of Applications in Wireless Sensor Networks: A gossip-based lifetime maximization approach. IEEE Transactions on Control Systems Technology 24 (5): 1828–1836.

      59 59 O'Donoghue, J. and Herbert, J. (2012). Data management within mHealth environments: patient sensors, mobile devices, and databases. Journal of Data and Information Quality 4 (1): 5.

      60 60 Lai, D., Begg, R.K., and Palaniswami, M. (eds.) (2011). Healthcare Sensor Networks: Challenges toward Practical Implementation. CRC Press.

      61 61 O'Donovan, T., O'Donoghue, J., Sreenan, C., et al., (2019) A context aware wireless body area network (BAN). https://www.ucc.ie/en/media/research/misl/2009publications/pervasive09.pdf (accessed 25 November 2019).

      2.1 Introduction

      The identification and measurement of human body biomarkers is a major goal in clinical diagnosis and disease monitoring. Nevertheless, prior to any measurement, advances in medical science to a large extent help in the recognition of abnormalities by looking at the symptoms and peripheral information. As an example, a number of procedures and measurements are needed to find out if the tiny medial temporal discharges originating within the hippocampus indicate any impending seizure. These clinical operations may involve imaging of the head using MRI (magnetic resonance imaging), taking multichannel electroencephalography (EEG) or magnetoencephalography (MEG) from the scalp, observing a patient's behaviour and movement for a substantial period of time, implanting subdural electrodes within the patient's brain, and checking their biological and even psychological reactions.

      This chapter elaborates on the most popular physical, physiological, biological, and behavioural symptoms; abnormalities; and diseases which mostly can be measured and quantified by means of multiple body sensors.

      In addition to the simple and obvious characteristics to describe a person, such as what they look like, their geometry, hair, and skin colour, there are additional attributes and perhaps more demanding factors in terms of their quantification such as those used in describing them and their actions. Among these factors are those often called biometrics. These include facial features, fingerprint, gait, voice, and other particular markers, such as skin spots.

      Face, gait, and joint face-gait recognition have been well researched by groups of researchers around the world [1]. Gait includes static features such as height, stride length, and silhouette bounding box lengths plus some dynamic features such as the frequency or time-frequency domain parameters like frequency and phase of a walk. Gait as a biometric can be used at long distances, it is nonintrusive, noninvasive, and hard to disguise [2].

      Extensive studies on biomechanical and clinical aspects of hundreds of limbs, joints, and muscles working together indicate that we can derive a reliable description of a person, unique to their way of walking. Moreover, gait can not only reveal the presence of certain sicknesses or moods, but also distinguish between genders. The variability of gait for a person is fairly consistent and not easily changed, while allowing for differentiation with others.

      Two different data recording modalities are normally used for gait analysis. One modality involves mounting or attaching proper sensors to the human body, while another uses frontal, lateral, or frontolateral video cameras to take the video of the walking subject and analyse it. The former type is intrusive and may affect the true gait motion. However, since the body movement is recorded more accurately, many applications in rehabilitative assessment effectively exploit that. In addition, using wired or wireless links between the sensors, the cooperation between the sensor signals can be a new platform in sensor networking research.

      In one research attempt [4] the number of sensors used for gait analysis has been reduced to one, which can be mounted above the ear. It has been shown that stride length and walking speed can be accurately estimated. Thus, during the rehabilitation process, the pattern of walking demonstrates the rehabilitation progress of a subject with a prosthetic limb.

      The use of a video camera requires skill in image processing in order to enable accurate extraction of movement features [5]. Furthermore, for video-based recognition of biometrics often more than one camera is needed to overcome occlusion problems. In practice, various combinations of biometric sensors may also be employed. Usually, two cameras are needed if frontoparallel gait is used. Many other biometrics can be extracted from the frontonormal plane. The problems of alignment and synchronisation are significant. If possible, single camera or monocular capture of video is preferred even if less data are recorded. To overcome this, Zhou and Bhanu use a profile view of a face with gait in order to use one camera at 3.3 m from the subject [6]. Of note is the work by Bazin that includes the ear and footfall as biometrics [7]. As another example, the frontonormal view allows one to use face and iris with gait for a robust recognition system, though some other problems, such as looming effect, make this modality a challenging and difficult case.

      Research about human gait has been extended to rehabilitative assessment for various disorders such as stroke, cerebral

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