A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


BibTex format

author = {Gu, X and Guo, Y and Deligianni, F and Lo, B and Yang, G-Z and Gu, X},
doi = {10.1109/TNNLS.2020.3009448},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {546--560},
title = {Cross-subject and cross-modal transfer for generalized abnormal gait pattern recognition},
url = {http://dx.doi.org/10.1109/TNNLS.2020.3009448},
volume = {32},
year = {2021}

RIS format (EndNote, RefMan)

AB - For abnormal gait recognition, pattern-specific features indicating abnormalities are interleaved with the subject-specific differences representing biometric traits. Deep representations are, therefore, prone to overfitting, and the models derived cannot generalize well to new subjects. Furthermore, there is limited availability of abnormal gait data obtained from precise Motion Capture (Mocap) systems because of regulatory issues and slow adaptation of new technologies in health care. On the other hand, data captured from markerless vision sensors or wearable sensors can be obtained in home environments, but noises from such devices may prevent the effective extraction of relevant features. To address these challenges, we propose a cascade of deep architectures that can encode cross-modal and cross-subject transfer for abnormal gait recognition. Cross-modal transfer maps noisy data obtained from RGBD and wearable sensors to accurate 4-D representations of the lower limb and joints obtained from the Mocap system. Subsequently, cross-subject transfer allows disentangling subject-specific from abnormal pattern-specific gait features based on a multiencoder autoencoder architecture. To validate the proposed methodology, we obtained multimodal gait data based on a multicamera motion capture system along with synchronized recordings of electromyography (EMG) data and 4-D skeleton data extracted from a single RGBD camera. Classification accuracy was improved significantly in both Mocap and noisy modalities.
AU - Gu,X
AU - Guo,Y
AU - Deligianni,F
AU - Lo,B
AU - Yang,G-Z
AU - Gu,X
DO - 10.1109/TNNLS.2020.3009448
EP - 560
PY - 2021///
SN - 1045-9227
SP - 546
TI - Cross-subject and cross-modal transfer for generalized abnormal gait pattern recognition
T2 - IEEE Transactions on Neural Networks and Learning Systems
UR - http://dx.doi.org/10.1109/TNNLS.2020.3009448
UR - http://hdl.handle.net/10044/1/81388
VL - 32
ER -