602
R. B. Pachori and V. Gupta
EMG signals [72]. The automated classification of hand movements using TQWTbased filter bank with EMG signals has been also proposed [73].
12.9.4 Postural Stability Analysis
The postural control is very useful for everyday movement and the central nervous
system provides sensory information for postural control. This system is used to
maintain a proper postural balance. Any postural imbalance may lead to instability,
falls, and injury. The center of pressure signals are commonly used to examine the
postural control [74]. These signals can be analyzed with various signal processing
methods. The method based on FBSE applied on the center of pressure signals has
been used for postural stability analysis [74]. The method for assessment of standing
postural stability in children has been also proposed [75]. The EMD method with
second-order difference plots has been used for postural time-series analysis [76].
12.9.5 Other Related Applications
The knee joint pathological conditions change the vibroathrographic (VAG) signals.
These VAG signals provide the abnormalities associated with knee joints. The
automated screening of knee joints using double density dual-tree complex WT has
been proposed [77]. The detection of direction of eyes movement has been possible
using EOG signals [78]. This detection provides help to various disabled persons.
References
1. J. Enderle, J. Brozino, Introduction to Biomedical Engineering (Academic Press, Burlington,
2012)
2. O.G. Martinsen, S. Grimnes, Bioimpedance and Bioelectricity Basics (Academic Press,
London, 2011)
3. R. Plonsey, R.C. Barr, Bioelectricity: a Quantitative Approach (Springer Science & Business
Media, New York, 2007)
4. A. Winter, Biomechanics and Motor Control of Human Movement (Wiley, Hoboken, 2009)
5. G.S. Firestein, R. Budd, S.E. Gabriel, I.B. McInnes, J.R. O’Dell, Kelley’s Textbook of
Rheumatology E-Book (Elsevier Health Sciences, London, 2012)
6. A.P. Turner, J.C. Pickup, Diabetes mellitus: biosensors for research and management. Biosensors 1(1), 85–115 (1985)
7. L.J. Blum, P.R. Coulet, Biosensor Principles and Applications (M. Dekker, New York, 1991)
8. T. Vo-Dinh, B. Cullum, Biosensors and biochips: advances in biological and medical diagnostics. Fresenius J. Anal. Chem. 366(6–7), 540–551 (2000)
9. J.-Y. Yoon, Introduction to Biosensors: from Electric Circuits to Immunosensors (Springer,
Cham, 2016)
R. B. Pachori and V. Gupta
EMG signals [72]. The automated classification of hand movements using TQWTbased filter bank with EMG signals has been also proposed [73].
12.9.4 Postural Stability Analysis
The postural control is very useful for everyday movement and the central nervous
system provides sensory information for postural control. This system is used to
maintain a proper postural balance. Any postural imbalance may lead to instability,
falls, and injury. The center of pressure signals are commonly used to examine the
postural control [74]. These signals can be analyzed with various signal processing
methods. The method based on FBSE applied on the center of pressure signals has
been used for postural stability analysis [74]. The method for assessment of standing
postural stability in children has been also proposed [75]. The EMD method with
second-order difference plots has been used for postural time-series analysis [76].
12.9.5 Other Related Applications
The knee joint pathological conditions change the vibroathrographic (VAG) signals.
These VAG signals provide the abnormalities associated with knee joints. The
automated screening of knee joints using double density dual-tree complex WT has
been proposed [77]. The detection of direction of eyes movement has been possible
using EOG signals [78]. This detection provides help to various disabled persons.
References
1. J. Enderle, J. Brozino, Introduction to Biomedical Engineering (Academic Press, Burlington,
2012)
2. O.G. Martinsen, S. Grimnes, Bioimpedance and Bioelectricity Basics (Academic Press,
London, 2011)
3. R. Plonsey, R.C. Barr, Bioelectricity: a Quantitative Approach (Springer Science & Business
Media, New York, 2007)
4. A. Winter, Biomechanics and Motor Control of Human Movement (Wiley, Hoboken, 2009)
5. G.S. Firestein, R. Budd, S.E. Gabriel, I.B. McInnes, J.R. O’Dell, Kelley’s Textbook of
Rheumatology E-Book (Elsevier Health Sciences, London, 2012)
6. A.P. Turner, J.C. Pickup, Diabetes mellitus: biosensors for research and management. Biosensors 1(1), 85–115 (1985)
7. L.J. Blum, P.R. Coulet, Biosensor Principles and Applications (M. Dekker, New York, 1991)
8. T. Vo-Dinh, B. Cullum, Biosensors and biochips: advances in biological and medical diagnostics. Fresenius J. Anal. Chem. 366(6–7), 540–551 (2000)
9. J.-Y. Yoon, Introduction to Biosensors: from Electric Circuits to Immunosensors (Springer,
Cham, 2016)