Non-Contact Early Warning of Shaking Palsy

Abstract

Parkinsonian gait is a defining feature of shaking palsy (SP) and it has one of the worse impacts on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system based on an S-band perception technique to classify abnormal gait and normal walking. Due to the differences in the Gaits of Parkinson’s patients compared with healthy persons, the wireless signals reflect and generates different variations at the receiver that could be used for SP diagnosis and classification. To detect a Parkinsonian gait, we first implement data preprocessing of the original data to obtain clear amplitude and phase information. Then, the feature extraction is carried out by principal component analysis (PCA). Finally, a support vector machine (SVM) classification algorithm is applied on collected data to classify the abnormal gait of SP patients compared with a normal gait. We evaluate the proposed system with different people, and the experimental outcomes show that the Parkinsonian gait detection of this training-based system achieves a high accuracy of above 90%. Moreover, the early warning of SP is achieved in a non-contact manner.

Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm

“…The system works, as AP pings at 20 packets / seconds and the receiver receive the WCI of different body motions in the form of matrix H (channel matrix) of 30 x 1 [13] [14]. The system captures the CFR of 30 subcarriers and the obtained CFR gives the information regarding frequencies and number of antennas used in the sensing process.…”

Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

“…As an alternative to context specific signal processing pathways, machine learning following a feature extraction step can be applied where there is a dataset available and known patterns need to be extracted [61] or to perform classification [53] , [67] , [68] , [70] (Ṁ, ③). Machine learning can be a powerful tool in the toolbox of target information extraction techniques, but a thorough analysis of the myriad of machine learning techniques is beyond the scope of this article.…”

“…With a combination of a small microphone and accelerometer placed externally on a patient's throat, inference of the interior structure and movement of the cartilage and bones can be made, in turn classifying swallowing health [69]. Microwaves reflected off people has been shown to be finesse enough to identify those who may have a gait that exhibits the characteristics associated with shaking palsy (a defining symptom of Parkinson patients) [70]. This type of research provides a rich source of insights into the potentials of side-channels for MDM.…”

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

“…Wireless sensing technology is used for the detection of Parkinsonian gait using S-band for classification of normal walking and abnormal gait. Additionally, the early diagnosis of shaking palsy (SP) symptoms in a non-contact manner was also achieved [ 58 ]. Patients suffering from dementia show signs of wandering behavior due to memory loss or boredom.…”

“…The SVM algorithm is widely applied because it is applicable to both linear and non-linear data. The classification accuracy achieved by ML and DL algorithms is over 90% [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. The average accuracy achieved by various non-contact sensing technologies to monitor health issues is shown in Figure 2 .…”