Parkinson's disease (PD) is a progressive and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson's patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multiresolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ∼87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ∼98% using data fusion.
Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.
The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data (91%) each time the trial was made. DT (17.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting.
Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems.
Monitoring Activities of Daily Livings (ADLs) has opened doors for numerous applications including patient monitoring, search & rescue, intrusion detection and so on. However, the parameters such as operating frequency, transmitting power, and antenna design are static where each application requires particular hardware applications. This paper lays the foundation for ADLs and presents the design of the testbed based on Universal Software Radio Peripheral (USRP) in conjunction with omni directional antenna, that can be used for detecting large scale body movements such as walking, sitting, standing, and critical events such as falls and small-scale movements. The core idea is to extract the channel state information (CSI) from the received signal since each body motion produces a unique CSI signature. In this context, we have performed various human activities such as walking, sitting on a chair etc. in indoor environment using two USRPs. The experimental results indicate that each body motion can be visually identified by examining the CSI data.
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