Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities

Journal article


Manna, S., Azhar, H. and Greace, A. 2023. Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities. Heliyon. 9 (4), p. e15210. https://doi.org/10.1016/j.heliyon.2023.e15210
AuthorsManna, S., Azhar, H. and Greace, A.
Abstract

Neuromuscular diseases cause abnormal joint movements and drastically alter gait patterns in patients. The analysis of abnormal gait patterns can provide clinicians with an in-depth insight into implementing appropriate rehabilitation therapies. Wearable sensors are used to measure the gait patterns of neuromuscular patients due to their non-invasive and cost-efficient characteristics. FSR and IMU sensors are the most popular and efficient options. When assessing abnormal gait patterns, it is important to determine the optimal locations of FSRs and IMUs on the human body, along with their computational framework. The gait abnormalities of different types and the gait analysis systems based on IMUs and FSRs have therefore been investigated. After studying a variety of research articles, the optimal locations of the FSR and IMU sensors were determined by analysing the main pressure points under the feet and prime anatomical locations on the human body. A total of seven locations (the big toe, heel, first, third, and fifth metatarsals, as well as two close to the medial arch) can be used to measure gate cycles for normal and flat feet. It has been found that IMU sensors can be placed in four standard anatomical locations (the feet, shank, thigh, and pelvis). A section on computational analysis is included to illustrate how data from the FSR and IMU sensors are processed. Sensor data is typically sampled at 100 Hz, and wireless systems use a range of microcontrollers to capture and transmit the signals. The findings reported in this article are expected to help develop efficient and cost-effective gait analysis systems by using an optimal number of FSRs and IMUs.

KeywordsGait abnormalities; Measurement of gait; Sensor location; Computational framework
Year2023
JournalHeliyon
Journal citation9 (4), p. e15210
PublisherElsevier
ISSN2405-8440
Digital Object Identifier (DOI)https://doi.org/10.1016/j.heliyon.2023.e15210
Official URLhttps://www.cell.com/heliyon/pdf/S2405-8440(23)02417-9.pdf
Related URLhttps://www.sciencedirect.com/science/article/pii/S2405844023024179?via%3Dihub
Publication dates
Print04 Apr 2023
Publication process dates
Accepted29 Mar 2023
Deposited24 Apr 2023
Publisher's version
License
File Access Level
Open
Output statusPublished
References

[1] V.L. Feigin, E. Nichols, T. Alam, M.S. Bannick, E. Beghi, N. Blake, et al., Global, regional, and national burden of neurological disorders, 1990–2016: a systematic
analysis for the Global Burden of Disease Study 2016, Lancet Neurol. 18 (5) (2019) 459–480.
[2] I. Bos, K. Wynia, J. Almansa, G. Drost, B. Kremer, J. Kuks, The prevalence and severity of disease-related disabilities and their impact on quality of life in
neuromuscular diseases, Disabil. Rehabil. 41 (14) (2019) 1676–1681.
[3] S. Li, G.E. Francisco, W.Z. Rymer, A new definition of poststroke spasticity and the interference of spasticity with motor recovery from acute to chronic stages,
Neurorehabil. Neural Repair. (2021), 15459683211011214.
[4] S. Li, G.E. Francisco, P. Zhou, Post-stroke hemiplegic gait: new perspective and insights, Front. Physiol. 9 (2018) 1021.
[5] L.M. Silva, N. Stergiou, The basics of gait analysis, Biomech. Gait Anal. 164 (2020) 231.
[6] K. Iyengar, G.K. Upadhyaya, R. Vaishya, V. Jain, COVID-19 and applications of smartphone technology in the current pandemic, Diabetes Metabol. Syndr.: Clin.
Res. Rev. 14 (5) (2020) 733–737.
[7] Y. Celik, S. Stuart, W.L. Woo, A. Godfrey, Gait analysis in neurological populations: Progression in the use of wearables, Med. Eng. Phys. 87 (2021) 9–29.
[8] P.B. Shull, W. Jirattigalachote, M.A. Hunt, M.R. Cutkosky, S.L. Delp, Quantified self and human movement: a review on the clinical impact of wearable sensing
and feedback for gait analysis and intervention, Gait Posture 40 (1) (2014) 11–19.
[9] K. Hori, Y. Mao, Y. Ono, H. Ora, Y. Hirobe, H. Sawada, et al., Inertial measurement unit-based estimation of foot trajectory for clinical gait analysis, Front.
Physiol. 10 (2020) 1530.
[10] C. Caramia, D. Torricelli, M. Schmid, A. Munoz-Gonzalez, J. Gonzalez-Vargas, F. Grandas, et al., IMU-based classification of Parkinson’s disease from gait: A
sensitivity analysis on sensor location and feature selection, IEEE J. Biomed. Health Inf. 22 (6) (2018) 1765–1774.
[11] M. Demonceau, A. Donneau, J. Croisier, E. Skawiniak, M. Boutaayamou, D. Maquet, et al., Contribution of a trunk accelerometer system to the characterization
of gait in patients with mild-to-moderate Parkinson’s disease, IEEE J. Biomed. Health Inf. 19 (6) (2015) 1803–1808.
[12] F. Arippa, B. Leban, M. Monticone, G. Cossu, C. Casula, M. Pau, A study on lower limb asymmetries in Parkinson’s disease during gait assessed through
kinematic-derived parameters, Bioengineering 9 (3) (2022) 120.
[13] C. Hansen, M. Beckbauer, R. Romijnders, E. Warmerdam, J. Welzel, J. Geritz, et al., Reliability of IMU-Derived Static Balance Parameters in Neurological
Diseases, Int. J. Environ. Res. Publ. Health 18 (7) (2021) 3644.
[14] B. Su, C. Smith, E. Gutierrez Farewik, Gait phase recognition using deep convolutional neural network with inertial measurement units, Biosensors 10 (9) (2020)
109.
[15] F. Narv´
aez, F. Arbito, ´ R. Proano, ˜ A Quaternion-Based Method to IMU-to-Body Alignment for Gait Analysis. Digital Human Modeling. Applications in Health,
Safety, Ergonomics, and Risk Management, Springer International Publishing, Cham, 2018, pp. 217–231.
[16] N.D. Strzalkowski, R.M. Peters, J.T. Inglis, L.R. Bent, Cutaneous afferent innervation of the human foot sole: what can we learn from single-unit recordings?
J. Neurophysiol. 120 (3) (2018) 1233–1246.
[17] P.R. Cavanagh, M.M. Rodgers, A. liboshi, Pressure distribution under symptom-free feet during barefoot standing, Foot Ankle 7 (5) (1987) 262–278.
[18] W. Hsu, T. Sugiarto, J. Chen, Y. Lin, The design and application of simplified insole-based prototypes with plantar pressure measurement for fast screening of
flat-foot, Sensors 18 (11) (2018) 3617.
[19] A.M. Ngueleu, A.K. Blanchette, L. Bouyer, D. Maltais, B.J. McFadyen, H. Moffet, et al., Design and accuracy of an instrumented insole using pressure sensors for
step count, Sensors 19 (5) (2019) 984.
[20] D. Cruz, C. Legaspi, D. Marcelino, R. Rosete, R. Sangalang, G. Suarez, A. Roxas, D. Serrano, R. dela Cruz, Joint gait kinematic and kinetic analysis using inertial
measurement units and plantar pressure sensor system, in: IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology,
Communication and Control, Environment, and Management (HNICEM), 2019, pp. 1–6.
[21] T.T. Duong, D. Uher, S.D. Young, T. Duong, M. Sangco, K. Cornett, J. Montes, D. Zanotto, Gaussian process regression for COP trajectory estimation in healthy
and pathological gait using instrumented insoles, in: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 9548–9553.
[22] S. Minto, D. Zanotto, E.M. Boggs, G. Rosati, S.K. Agrawal, Validation of a footwear-based gait analysis system with action-related feedback, IEEE Trans. Neural
Syst. Rehabil. Eng. 24 (9) (2015) 971–980.
[23] K.S. Tee, Y.S.H. Javahar, H. Saim, W.N.W. Zakaria, S.B.M. Khialdin, H. Isa, et al., A portable insole pressure mapping system, Telkomnika 15 (4) (2017)
1493–1500.
[24] A.R. Anwary, H. Yu, M. Vassallo, Optimal foot location for placing wearable IMU sensors and automatic feature extraction for gait analysis, IEEE Sens. J. 18 (6)
(2018) 2555–2567.
[25] L. Shu, T. Hua, Y. Wang, Q. Li, D.D. Feng, X. Tao, In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array, IEEE Trans.
Inf. Technol. Biomed. 14 (3) (2010) 767–775.
[26] A.M. Tahir, M.E. Chowdhury, A. Khandakar, S. Al-Hamouz, M. Abdalla, S. Awadallah, et al., A systematic approach to the design and characterization of a smart
insole for detecting vertical ground reaction force (vGRF) in gait analysis, Sensors 20 (4) (2020) 957.
[27] H. Kim, Y. Kang, D.R. Valencia, D. Kim, An integrated system for gait analysis using FSRs and an IMU, in: IEEE International Conference on Robotic Computing
(IRC), 2018, pp. 347–351.
[28] M. Burnfield, Gait analysis: normal and pathological function, J. Sports Sci. Med. 9 (2) (2010) 353.
[29] V.K. Nandikolla, R. Bochen, S. Meza, A. Garcia, Experimental gait analysis to study stress distribution of the human foot, J. Med. Eng. 2017 (2017).
[30] D.D. Espy, F. Yang, T. Bhatt, Y. Pai, Independent influence of gait speed and step length on stability and fall risk, Gait Posture 32 (3) (2010) 378–382.
[31] D.A. Winter, Biomechanics and Motor Control of Human Movement, John Wiley & Sons, 2009.
[32] W. Pirker, R. Katzenschlager, Gait disorders in adults and the elderly, Wien Klin Wochenschr 129 (3) (2017) 81–95.
[33] M.R. Lim, R.C. Huang, A. Wu, F.P. Girardi, F.P. Cammisa Jr., Evaluation of the elderly patient with an abnormal gait, JAAOS-J. Am. Acad. Orthopaedic Surg. 15
(2) (2007) 107–117.
[34] H.S. Lee, H. Ryu, S. Lee, J. Cho, S. You, J.H. Park, et al., Analysis of gait characteristics using hip-knee cyclograms in patients with hemiplegic stroke, Sensors 21
(22) (2021) 7685.
[35] W. Mostertz, Quantifying Antalgic Gait Knee Function using Inertial Sensor Technology, Clemson University, 2008. Doctoral dissertation.
[36] R.K. Avvari, M.K. Baig, T. Arunachalam, Gait analysis: an effective tool to measure human performance, in: Advances in Computational Approaches in
Biomechanics: IGI Global, 2022, pp. 65–87.
[37] P. Bhargava, P. Shrivastava, S.P. Nagariya, Assessment of changes in gait parameters and vertical ground reaction forces after total hip arthroplasty, Indian J.
Orthopaedics 41 (2) (2007) 158.
[38] W. Rueangsirarak, J. Zhang, N. Aslam, E.S. Ho, H.P. Shum, Automatic musculoskeletal and neurological disorder diagnosis with relative joint displacement from
human gait, IEEE Trans. Neural Syst. Rehabil. Eng. 26 (12) (2018) 2387–2396.
[39] T.T. Duong, S. Goldman, H. Zhang, R. Salazar, S. Beenders, K.M. Cornett, J.M. Bain, J. Montes, D. Zanotto, Validation of insole-based gait analysis system in
young children with a neurodevelopmental disorder and autism traits, in: 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and
Biomechatronics (BioRob), 2020, pp. 715–720.
[40] S.J.M. Bamberg, A.Y. Benbasat, D.M. Scarborough, D.E. Krebs, J.A. Paradiso, Gait analysis using a shoe-integrated wireless sensor system, IEEE Trans. Inf.
Technol. Biomed. 12 (4) (2008) 413–423.
[41] E. Abdulhay, N. Arunkumar, K. Narasimhan, E. Vellaiappan, V. Venkatraman, Gait and tremor investigation using machine learning techniques for the diagnosis
of Parkinson disease, Future Gener. Comput. Syst. 83 (2018) 366–373.
[42] B. Müller, W. Ilg, M.A. Giese, N. Ludolph, Validation of enhanced kinect sensor based motion capturing for gait assessment, PloS One 12 (4) (2017), e0175813.
[43] A. Pfister, A.M. West, S. Bronner, J.A. Noah, Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis, J. Med. Eng. Technol. 38
(5) (2014) 274–280.
[44] Y. Cha, K. Song, J. Shin, D. Kim, Gait analysis system based on slippers with flexible piezoelectric sensors, in: IEEE international conference on robotics and
biomimetics (ROBIO), 2018, pp. 2479–2484.
[45] P. Aqueveque, E. Germany, R. Osorio, F. Pastene, Gait segmentation method using a plantar pressure measurement system with custom-made capacitive sensors,
Sensors 20 (3) (2020) 656.
[46] J. Cheng, O. Amft, G. Bahle, P. Lukowicz, Designing sensitive wearable capacitive sensors for activity recognition, IEEE Sens. J. 13 (10) (2013) 3935–3947.
[47] S. Wang, F.C. Lee, Analysis and applications of parasitic capacitance cancellation techniques for EMI suppression, IEEE Trans. Ind. Electron. 57 (9) (2009)
3109–3117.
[48] T. Seel, J. Raisch, T. Schauer, IMU-based joint angle measurement for gait analysis, Sensors 14 (4) (2014) 6891–6909.
[49] Pedalvatar, An IMU-based real-time body motion capture system using foot rooted kinematic model, in: 2014 IEEE/RSJ International Conference on Intelligent
Robots and Systems, IEEE, 2014.
[50] M. Demonceau, A. Donneau, J. Croisier, E. Skawiniak, M. Boutaayamou, D. Maquet, et al., Contribution of a trunk accelerometer system to the characterization
of gait in patients with mild-to-moderate Parkinson’s disease, IEEE J. Biomed. Health Inf. 19 (6) (2015) 1803–1808.
[51] R. Romijnders, E. Warmerdam, C. Hansen, J. Welzel, G. Schmidt, W. Maetzler, Validation of IMU-based gait event detection during curved walking and turning
in older adults and Parkinson’s Disease patients, J. Neuroeng. Rehabil. 18 (1) (2021) 1–10.
[52] D. Lukˇsys, D. Jatuˇzis, G. Jonaitis, J. Griˇskeviˇcius, Application of continuous relative phase analysis for differentiation of gait in neurodegenerative disease,
Biomed. Signal Process. Control 67 (2021), 102558.
[53] J.M. Hausdorff, Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling, Chaos:
Interdiscipl. J. Nonlinear Sci. 19 (2) (2009), 026113.
[54] Y. Celik, S. Stuart, W.L. Woo, E. Sejdic, A. Godfrey, Multi-modal gait: a wearable, algorithm and data fusion approach for clinical and free-living assessment, Inf.
Fusion 78 (2022) 57–70.
[55] T.T. Pham, Y.S. Suh, Conditional generative adversarial network-based regression approach for walking distance estimation using waist-mounted inertial
sensors, in: IEEE Transactions on Instrumentation and Measurement, 2022.
[56] W. Kong, J. Lin, L. Waaning, S. Sessa, S. Cosentino, D. Magistro, M. Zecca, R. Kawashima, A. Takanishi, Comparison of gait event detection from shanks and feet
in single-task and multi-task walking of healthy older adults, in: IEEE International Conference on Robotics and Biomimetics (ROBIO), 2016, pp. 2063–2068.
[57] D. Trojaniello, A. Cereatti, E. Pelosin, L. Avanzino, A. Mirelman, J.M. Hausdorff, et al., Estimation of step-by-step spatio-temporal parameters of normal and
impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait, J. Neuroeng. Rehabil. 11 (1)
(2014) 1–12.
[58] R. Romijnders, E. Warmerdam, C. Hansen, J. Welzel, G. Schmidt, W. Maetzler, Validation of IMU-based gait event detection during curved walking and turning
in older adults and Parkinson’s Disease patients, J. Neuroeng. Rehabil. 18 (1) (2021) 1–10.
[59] M. Saito, K. Nakajima, C. Takano, Y. Ohta, C. Sugimoto, R. Ezoe, et al., An in-shoe device to measure plantar pressure during daily human activity, Med. Eng.
Phys. 33 (5) (2011) 638–645.
[60] H. Zhu, N. Maalej, J.G. Webster, W.J. Tompkins, P. Bach-y-Rita, J.J. Wertsch, An umbilical data-acquisition system for measuring pressures between the foot
and shoe, IEEE Trans. Biomed. Eng. 37 (9) (1990) 908–911.
[61] A.M. Howell, T. Kobayashi, H.A. Hayes, K.B. Foreman, S.J.M. Bamberg, Kinetic gait analysis using a low-cost insole, IEEE Trans. Biomed. Eng. 60 (12) (2013)
3284–3290.
[62] P. Catalfamo, D. Moser, S. Ghoussayni, D. Ewins, Detection of gait events using an F-Scan in-shoe pressure measurement system, Gait Posture 28 (3) (2008)
420–426.
[63] Y.C. Han, K.I. Wong, I. Murray, Gait phase detection for normal and abnormal gaits using IMU, IEEE Sens. J. 19 (9) (2019) 3439–3448.
[64] J. Zhao, A Review of Wearable IMU (Inertial-Measurement-Unit)-based Pose Estimation and Drift Reduction Technologies, J. Phys. Conf. Ser. 1087 (4) (2018),
42003.
[65] S. Qiu, H. Zhao, N. Jiang, Z. Wang, L. Liu, Y. An, et al., Multi-sensor information fusion based on machine learning for real applications in human activity
recognition: State-of-the-art and research challenges, Inf. Fusion 80 (2022) 241–265.
[66] P. Ippersiel, S.M. Robbins, P.C. Dixon, Lower-limb coordination and variability during gait: The effects of age and walking surface, Gait Posture 85 (2021)
251–257.
[67] P. Bet, P.C. Castro, M.A. Ponti, Fall detection and fall risk assessment in older person using wearable sensors: A systematic review, Int. J. Med. Inf. 130 (2019),
103946.
[68] G. Forbes, S. Massie, S. Craw, Fall prediction using behavioural modelling from sensor data in smart homes, Artif. Intell. Rev. 53 (2) (2020) 1071–1091.
[69] J. Yu, S. Zhang, A. Wang, W. Li, Z. Ma, X. Yue, Humanoid Control of Lower Limb Exoskeleton Robot Based on Human Gait Data with Sliding Mode Neural
Network, CAAI Transactions on Intelligence Technology, 2022.
[70] Y. Sun, R. Huang, J. Zheng, D. Dong, X. Chen, L. Bai, et al., Design and speed-adaptive control of a powered geared five-bar prosthetic knee using bp neural
network gait recognition, Sensors 19 (21) (2019) 4662.
[71] J. Marín, T. Blanco, J.J. Marín, A. Moreno, E. Martitegui, J.C. Aragü´es, Integrating a gait analysis test in hospital rehabilitation: A service design approach, PloS
One 14 (10) (2019), e0224409.
[72] R.D. Gurchiek, R.H. Choquette, B.D. Beynnon, J.R. Slauterbeck, T.W. Tourville, M.J. Toth, et al., Open-source remote gait analysis: A post-surgery patient
monitoring application, Sci. Rep. 9 (1) (2019) 1–10.
[73] J. Li, Z. Wang, S. Qiu, H. Zhao, Q. Wang, D. Plettemeier, et al., Using body sensor network to measure the effect of rehabilitation therapy on improvement of
lower limb motor function in children with spastic diplegia, IEEE Trans. Instrum. Meas. 69 (11) (2020) 9215–9227.
[74] B. Fuentes, M.A. de Lecinana, ˜ P. Calleja-Castano, ˜ J. Carneado-Ruiz, J. Egido-Herrero, A. Gil-Núnez, ˜ et al., Impact of the COVID-19 pandemic on the organisation
of stroke care, Madrid Stroke Care Plan. Neurol. (English Edition) 35 (6) (2020) 363–371.
[75] J. An, J. Kim, E.C. Lai, B.C. Lee, Effects of a smartphone-based wearable telerehabilitation system for in-home dynamic weight-shifting balance exercises by
individuals with Parkinson’s disease, in: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020,
pp. 5678–5681.
[76] S. Thomas, The new NHS landscape, Br. J. Neurosci. Nurs. 17 (4) (2021) 160–163.
[77] A. Patel, V. Berdunov, Z. Quayyum, D. King, M. Knapp, R. Wittenberg, Estimated societal costs of stroke in the UK based on a discrete event simulation, Age
Ageing 49 (2) (2020) 270–276.

Permalink -

https://repository.canterbury.ac.uk/item/94671/optimal-locations-and-computational-frameworks-of-fsr-and-imu-sensors-for-measuring-gait-abnormalities

Download files


Publisher's version
PIIS2405844023024179.pdf
License: CC BY-NC-ND 4.0
File access level: Open

  • 96
    total views
  • 30
    total downloads
  • 2
    views this month
  • 2
    downloads this month

Export as

Related outputs

Investigating security issues (multilayer attacks) on IoT devices using machine learning
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2024. Investigating security issues (multilayer attacks) on IoT devices using machine learning.
Safeguarding IoMT: Semi-automated Intrusion Detection System (SAIDS) for detecting multilayer attacks
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2024. Safeguarding IoMT: Semi-automated Intrusion Detection System (SAIDS) for detecting multilayer attacks.
Metaverse application forensics: Unravelling the virtual truth
Azhar, H. and Rush-Gadsby, O. Metaverse application forensics: Unravelling the virtual truth. in: Cybersecurity Challenges in the Age of AI, Space Communications and Cyborgs Proceedings of the 15th International Conference on Global Security, Safety and Sustainability, London, October 2023 Cham Springer. pp. 399-414
Transformer-based Models for Enhanced Amur Tiger Re-Identification
Bai, Xufeng, Islam, Tasmina and Bin Azhar, M A Hannan 2024. Transformer-based Models for Enhanced Amur Tiger Re-Identification. in: 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI) IEEE.
Trustworthy Insights: A Novel Multi-Tier Explainable framework for ambient assisted living
Kasirajan, M., Azhar, H. and Turner, S. 2023. Trustworthy Insights: A Novel Multi-Tier Explainable framework for ambient assisted living . https://doi.org/10.1109/TrustCom60117.2023.00357
Integration of graduate employability skills through industry outsourced CDIO project
Manna, S., Joyce, N. and Nortcliffe, A. 2023. Integration of graduate employability skills through industry outsourced CDIO project. in: Lyng, R., Bennedsen, J., Bettaied, L., Bodsberg, N. R., Edstrom, K., Guojonsdottir, M. S., Roslof, J., Solbjord, O. K. and Oien, G. (ed.) The 19th CDIO International Conference: Proceedings - Full Papers NTNU SEED. pp. 425-435
Assistive telehealth systems for neurorehabilitation
Azhar, H. 2023. Assistive telehealth systems for neurorehabilitation.
Spying on kids’ smart devices: Beware of security vulnerabilities!
Azhar, H., Smith, D. and Cain, A. 2023. Spying on kids’ smart devices: Beware of security vulnerabilities! in: Jahankhani, H. (ed.) Cybersecurity in the Age of Smart Societies Proceedings of the 14th International Conference on Global Security, Safety and Sustainability, London, September 2022 Springer. pp. 123-140
Cyber threats and exploits during the pandemic
Lo, J. and Azhar, H. Cyber threats and exploits during the pandemic. ASEAN Tech and Security, Singapore .
Machine learning-based solutions for securing IoT systems against multilayer attacks
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Machine learning-based solutions for securing IoT systems against multilayer attacks. in: Singh Tomar, R., Verma, S., Kumar Chaurasia, B., Singh, V., Abawajy, J. H., Akashe, S., Hsiung, Pao-Ann and Prasad, R. (ed.) Communication, Networks and Computing Third International Conference, CNC 2022, Gwalior, India, December 8–10, 2022, Proceedings, Part I Cham Springer. pp. 140-153
Investigating the security issues of multi-layer IoT attacks using machine learning techniques
Al Sukhni, Badeea, Dave, Jugal M., Manna, Soumya K. and Zhang, Leishi 2022. Investigating the security issues of multi-layer IoT attacks using machine learning techniques. in: 2022 Human-Centered Cognitive Systems (HCCS) IEEE.
Z is for Zoombombing
Azhar, H. 2022. Z is for Zoombombing. Medium.
Progressive web app for real-time doctor-patient communication and searchable health conditions
Hannan Bin Azhar, M A and Mohan, Joseph Thomas 2022. Progressive web app for real-time doctor-patient communication and searchable health conditions. 2022 E-Health and Bioengineering Conference (EHB). https://doi.org/10.1109/EHB55594.2022.9991288
Investigating the security issues of multi-layer IoMT attacks using machine learning techniques
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Investigating the security issues of multi-layer IoMT attacks using machine learning techniques.
Accuracy and repeatability study of an elbow exoskeleton for multistage exercises
Manna, Soumya K 2022. Accuracy and repeatability study of an elbow exoskeleton for multistage exercises. in: 2022 20th International Conference on Mechatronics - Mechatronika (ME) IEEE.
A smart and home-based telerehabilitation tool for patients with neuromuscular disorder
Manna, Soumya K., Hannan, M. A., Azhar, B., Smith, D. and Islam, T. 2022. A smart and home-based telerehabilitation tool for patients with neuromuscular disorder. IEEE. https://doi.org/10.1109/iecbes54088.2022.10079410
Evaluation of students’ performance in CDIO projects through blended learning
Manna, S., Battikh, N., Nortcliffe, A. and Camm, J. 2022. Evaluation of students’ performance in CDIO projects through blended learning.
Forensic investigations of Google Meet and Microsoft Teams – two popular conferencing tools in the Pandemic
Azhar, H., Timms, J. and Tilley, B. 2022. Forensic investigations of Google Meet and Microsoft Teams – two popular conferencing tools in the Pandemic. in: Digital Forensics and Cyber Crime Springer Nature. pp. 20-34
Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things
Herring, Samuel, Azhar, M. A. Hannan Bin and Sakel, Mohamed 2022. Tele-tDCS: A Novel Tele-neuromodulation Framework using Internet of Medical Things. in: Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIODEVICES Setúbal, Portugal SCITEPRESS - Science and Technology Publications. pp. 84-93
Automatic identification of non-biting midges (Chironomidae) using object detection and deep learning techniques
Hollister, Jack, Vega, Rodrigo and Azhar, M. A. Hannan Bin 2022. Automatic identification of non-biting midges (Chironomidae) using object detection and deep learning techniques. in: Marsico, Maria D., Sanniti de Baja, Gabriella and Fred, Ana (ed.) Proceedings of the 11 International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications.
A smart and secure IoMT tele-neurorehabilitation framework for post-stroke patients
Manna, S., Azhar, H. and Sakel, M. 2022. A smart and secure IoMT tele-neurorehabilitation framework for post-stroke patients. in: Bhaumik, S., Chattopadhyay, S., Chattopadhyay, T. and Bhattacharya, S. (ed.) Proceedings of International Conference on Industrial Instrumentation and Control ICI2C 2021 Singapore Springer. pp. 11-20
Enhancing hands-on skills under capstone CDIO project using blended learning approach
Manna, S., Battikh, N. and Camm, J. 2021. Enhancing hands-on skills under capstone CDIO project using blended learning approach . Sheffield
An inclusive student-led online class test during the pandemic
Manna, S. and Azhar, H. 2021. An inclusive student-led online class test during the pandemic . Assessment and Feedback Symposium 2021.
A forensic tool to acquire radio signals using software defined radio
Azhar, H. and Abadia, G. 2021. A forensic tool to acquire radio signals using software defined radio. in: Security and Privacy in Communication Networks : 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6-9, 2021, Proceedings, Part I Springer.
Post-pandemic digital education: Investigating smart workspaces within the higher education sector
Azhar, M A Hannan Bin, Lepore, Emily Louise and Islam, T. 2021. Post-pandemic digital education: Investigating smart workspaces within the higher education sector. Proceedings of the BCS 34th British HCI Conference 2021. 34, pp. 284-288. https://doi.org/10.14236/ewic/hci2021.30
A study of user experiences and network analysis on anonymity and traceability of bitcoin transactions
Azhar, M.A.H.B and Whitehead, R.V. 2021. A study of user experiences and network analysis on anonymity and traceability of bitcoin transactions. EAI Endorsed Transactions on Security and Safety. https://doi.org/10.4108/eai.30-4-2021.169577
BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients
Azhar, H. 2021. BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients.
Adaptive and flexible online learning during Covid19 lockdown
Manna, S., Nortcliffe, A., Sheikholeslami, G. and Richmond-Fuller, A. 2021. Adaptive and flexible online learning during Covid19 lockdown.
Developing engineering growth mindset through CDIO outreach activities
Manna, S., Nortcliffe, A. and Sheikholeslami, G. 2020. Developing engineering growth mindset through CDIO outreach activities. in: Proceedings of the 16th International CDIO Conference Gothenburg, Sweden CDIO.
Forensic investigations of popular ephemeral messaging applications on Android and iOS platforms
Azhar, H., Cox, R. and Chamberlain, A. 2020. Forensic investigations of popular ephemeral messaging applications on Android and iOS platforms. International Journal on Advances in Security. 13 (1 & 2), pp. 41 - 53.
Design of a game-based rehabilitation system using Kinect sensor
Manna, S. and Dubuy, V. N. 2019. Design of a game-based rehabilitation system using Kinect sensor. Minneapolis, MN, USA ASME.
Assessment of joint parameters in a Kinect sensor based rehabilitation game
Manna, S. and Dubey, V. N. 2019. Assessment of joint parameters in a Kinect sensor based rehabilitation game. Anaheim, California, USA ASME.
Rehabilitation strategy for post-stroke recovery using an innovative elbow exoskeleton
Manna, S. and Dubey, V. N. 2019. Rehabilitation strategy for post-stroke recovery using an innovative elbow exoskeleton. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 233 (6), pp. 668-680. https://doi.org/10.1177/0954411919847058
A portable elbow exoskeleton for three stages of rehabilitation
Manna, S. and Dubey, V. N. 2019. A portable elbow exoskeleton for three stages of rehabilitation. Journal of Mechanisms and Robotics. 11 (6), p. 065002. https://doi.org/10.1115/1.4044535
Comparisons of forensic tools to recover ephemeral data from iOS apps used for cyberbullying
Chamberlain, A. and Azhar, H. 2019. Comparisons of forensic tools to recover ephemeral data from iOS apps used for cyberbullying. in: CYBER 2019, The Fourth International Conference on Cyber-Technologies and Cyber-Systems IARIA. pp. 88-93
Recovery of forensic artefacts from a smart home IoT ecosystem
Azhar, H. and Bate, S. 2019. Recovery of forensic artefacts from a smart home IoT ecosystem. in: CYBER 2019, The Fourth International Conference on Cyber-Technologies and Cyber-Systems IARIA. pp. 94-99
BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients
Casey, A., Azhar, H., Grzes, M. and Sakel, M. 2019. BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients. Disability and Rehabilitation: Assistive Technology. 16 (5), pp. 525-537. https://doi.org/10.1080/17483107.2019.1683239
Effects of students’ preferences in use of lighting and temperature on productivity in a university setting
Azhar, H., Islam, T. and Alfieri, M. 2019. Effects of students’ preferences in use of lighting and temperature on productivity in a university setting. in: Zheng, P., Callaghan, V., Crawford, D., Kymalainen, T. and Reyes-Munoz, A. (ed.) EAI International Conference on Technology, Innovation, Entrepreneurship and Education Springer.
Use of wearable technology to measure emotional responses amongst tennis players
Azhar, H., Nelson, T. and Casey, A. 2019. Use of wearable technology to measure emotional responses amongst tennis players. in: Zheng, P., Callaghan, V., Crawford, D., Kymalainen, T. and Reyes-Munoz, A. (ed.) EAI International Conference on Technology, Innovation, Entrepreneurship and Education Springer.
Drone forensic analysis using open source tools
Azhar, H., Barton, T. and Islam, T. 2018. Drone forensic analysis using open source tools. Journal of Digital Forensics, Security and Law. 13 (1), pp. 7-30.
A cost-effective BCI assisted technology framework for neurorehabilitation
Azhar, H., Casey, A. and Sakel, M. 2018. A cost-effective BCI assisted technology framework for neurorehabilitation.
An investigation on forensic opportunities to recover evidential data from mobile phones and personal computers
Naughton, P. and Azhar, H. 2017. An investigation on forensic opportunities to recover evidential data from mobile phones and personal computers.
BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients
Azhar, H., Barton, T., Casey, A. and Sakel, M. 2017. BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients. Research and Knowledge Exchange Conference 2017.
Open source forensics for a multi-platform drone system
Barton, T. and Azhar, H. 2018. Open source forensics for a multi-platform drone system. in: Matousek, P. and Schmiedecker, M. (ed.) 9th EAI International Conference on Digital Forensics & Cyber Crime Springer. pp. 83-96
Evaluation of the MPS Predictive Policing Trial (redacted)
Bryant, R., Azhar, H., Blackburn, B. and Falade, M. 2015. Evaluation of the MPS Predictive Policing Trial (redacted).
Forensic analysis of popular UAV systems
Barton, T. and Azhar, H. 2017. Forensic analysis of popular UAV systems. Emerging Security Technologies (EST), 2017 Seventh International Conference on. https://doi.org/10.1109/EST.2017.8090405
A wearable brain-computer interface controlled robot
Azhar, H., Badicioiu, A. and Barton, T. 2016. A wearable brain-computer interface controlled robot.
Forensic analysis of the recovery of Wickr’s ephemeral data on Android platforms
Barton, T. and Azhar, H. 2016. Forensic analysis of the recovery of Wickr’s ephemeral data on Android platforms. in: Klemas, T. and Falk, R. (ed.) CYBER 2016 : The First International Conference on Cyber-Technologies and Cyber-Systems IARIA. pp. 35-40
Forensic analysis of secure ephemeral messaging applications on Android platforms
Azhar, H. and Barton, T. 2017. Forensic analysis of secure ephemeral messaging applications on Android platforms. in: Global Security, Safety and Sustainability - The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings Springer.
Usability and performance measure of a consumer-grade brain computer interface system for environmental control by neurological patients
Deravi, F., Ang, C., Azhar, H., Al-Wabil, A., Philips, M. and Sakel, M. 2015. Usability and performance measure of a consumer-grade brain computer interface system for environmental control by neurological patients. International Journal of Engineering and Technology Innovation (IJETI). 5 (3), pp. 165-177.
Criticality dispersion in swarms to optimize n-tuples
Azhar, H., Deravi, F. and Dimond, K. 2008. Criticality dispersion in swarms to optimize n-tuples. in: GECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation New York Association for Computing Machinery. pp. 1-8
Particle swarm intelligence to optimize the learning of n-tuples
Azhar, H., Deravi, F. and Dimond, K. 2008. Particle swarm intelligence to optimize the learning of n-tuples. Journal of Intelligent Systems. 17 (S), pp. 169-196. https://doi.org/10.1515/JISYS.2008.17.S1.169
Automatic identification of wildlife using local binary patterns
Azhar, H., Hoque, S. and Deravi, F. 2012. Automatic identification of wildlife using local binary patterns. in: IET Conference on Image Processing (IPR 2012) Institute of Engineering and Technology. pp. 5-11
Zoometrics - biometric identification of wildlife using natural body marks
Hoque, S., Azhar, H. and Deravi, F. 2011. Zoometrics - biometric identification of wildlife using natural body marks. International Journal of Bio-Science and Bio-Technology. 3 (3), pp. 45-53.
Forensic acquisitions of WhatsApp data on popular mobile platforms
Shortall, A. and Azhar, H. 2015. Forensic acquisitions of WhatsApp data on popular mobile platforms. in: Proceedings of the Sixth International Conference on Emerging Security Technologies IEEE. pp. 13-17