**Qiyu Zhu 1, Lei Guan 2, Muhammad Bilal Khan <sup>1</sup> and Xiaodong Yang 1,\***


Received: 17 December 2019; Accepted: 22 January 2020; Published: 27 January 2020

**Abstract:** Huntington's disease (HD) is a rare genetic disorder that cannot be cured by current medical techniques. With the development of the disease, the life of patients will become more and more difficult. It is necessary to timely and effectively evaluate the development of the patient's condition based on the patient's clinical symptoms to help doctors to formulate a reasonable and effective treatment plan, alleviate the condition, and improve the quality of life, which reflects humane care. Currently, wearable devices or video surveillance are generally used to monitor the patients, and these schemes have some disadvantages. This paper presents a new method to monitor patients with HD using wireless sensing technology. Firstly, experimental data were collected by the self-developed microwave sensing platform (MSP), and then the data were preprocessed. Finally, support vector machine (SVM) and random forest (RF) algorithms were used to train the model. The MSP system continuously monitors patients in a non-contact way, which will not bring inconvenience to patients' lives, and will not involve privacy issues. The experimental results show that the prediction accuracy of SVM can be as high as 98.0% and that of RF can be as high as 96.7%, which proves the feasibility of the technical scheme described in this paper.

**Keywords:** HD; MSP; RF; SVM; wireless sensing technology

## **1. Introduction**

Huntington's disease (HD) is a very rare autosomal dominant genetic disease. The cause of the disease is the mutation of Huntington gene on chromosome 4 of the patient, resulting in the variation of protein, which leads to the change of normal neural function through the related molecular mechanism [1]. The diagnosis of this disease depends on genetic testing. It usually develops around 35–45 years old [2]. The disease degenerates the patient's physical and psychological intelligence during the working-age and places a heavy burden on the patient. The clinical symptoms of HD mainly fall into three categories: motor symptoms, cognitive symptoms, and mental symptoms [3–5]. The typical manifestation of motor symptoms is that the fingers appear to play piano-like movements, accompanied by weird facial expressions. If the trunk is involved, the patient can have a dance-like gait. As the disease progresses, other body movements will also become slow and uncoordinated. In the end, the patient's entire system will be affected, making it difficult for the patient to complete simple daily actions such as walking, talking, eating, dressing, and washing. Cognitive symptoms sometimes appear many years earlier than motor symptoms. In the early stages of cognitive symptoms, patients do not only suffer from episodic memory, but also have significant functional dysfunction and do not understand the meaning of the speaker's language. As the disease progresses, the patient's dementia symptoms worsen. However, even if the condition is severe, the patient retains some cognitive functions. The third type of symptoms are mental symptoms. The mental symptoms of patients are often earlier than or synchronous with abnormal motor symptoms, mainly manifested

as depression, often accompanied by insomnia, anorexia, and personality changes. In the later stage, patients will gradually experience hallucinations, delusions, paranoia, and aggressive behavior.

HD is characterized by complex clinical symptoms, progressive deterioration of the patient's condition, and usually death 15–20 years after onset [6]. At present, there is no effective treatment for this disease. The purpose of treatment is to improve the quality of life of patients. Several existing treatment methods can be classified as cause treatment and symptomatic treatment [7]. Cause treatment includes direct gene therapy and other indirect molecular therapy, this method cannot be realized at present, but a lot of research has been carried out. The symptomatic treatment is that the doctor obtains the clinical symptoms of the patient through visual observation and other methods and diagnoses the patient's condition against the HD Scale [8], so as to formulate a drug treatment plan to directly alleviate the patient's symptoms.

Drug therapy is currently the main treatment for HD. In order to confirm whether drug therapy is effective, doctors need to continue to follow up the effect of drug therapy, so as to improve the treatment. But this treatment has a disadvantage; the doctor's judgment of the patient is mainly based on the naked eye and the HD Scale. The diagnosis result can easily be affected by the subjective consciousness of the doctor. A promising solution is needed to continuously monitor the patient and provide an objective diagnosis basis. At the same time, the mental symptoms of patients are unstable, and patients are prone to hallucinations and aggressive behavior. In order to avoid self-injury, we also need to carry out continuous monitoring and give timely psychotherapy to patients.

In order to solve the above-mentioned problems, this paper proposes a new method to monitor HD patients using wireless sensing technology and demonstrates the feasibility of the proposed method. We independently developed the microwave sensing platform (MSP) monitoring tool, which is composed of a transmitter module and a receiver module. It can be directly installed in the indoor environment, without any contact with the patient, and it can be completely monitored in a non-contact way. The working process of MSP is: the transmitting module transmits the wireless signal in C-band, the receiving module receives the wireless signal and extracts the channel state information (CSI) data through channel estimation. After collecting CSI data, we first carried out a series of data preprocessing steps, including removing outliers and wavelet transform de-noising. Then we extracted the features of the preprocessed data to make the sample set. Finally, we used support vector machine (SVM) [9] and random forest (RF) [10] to train the classification model, respectively. The trained classification model can effectively distinguish the behavior of HD patients and normal people. The experimental results show that the accuracy of SVM and RF is 98.0% and 96.7%, respectively, which proves that the methods described in this paper can effectively monitor HD patients.

The rest of this paper is organized as follows. The Section 2 will introduce the current research on the monitoring of HD and make a brief comparison with the experimental scheme proposed in this paper. In the Section 3, we will introduce the principle of wireless sensing technology, and in the Section 4, we will describe the experimental scheme design. In the Section 5, we will describe the data processing flow. In the Section 6, we will discuss the experimental results and draw conclusions in the Section 7.

#### **2. Related Work**

At present, a large number of scholars have carried out related research on monitoring patients with HD using wearable devices. For example, Dinesh et al. reported a preliminary study to analyze motor symptoms associated with HD and Parkinson's disease based on sensor signal detection and data analysis. They use a light-weight, low-power sensor to monitor the motor symptoms of patients. The sensor adheres to the limbs and chest of patients like a tattoo and can continuously monitor patients for 48 hours. The experimental results show that the sensor can capture different clear signals of different clinical symptoms [11]. Bennasar et al. proposed an HD upper limb dyskinesia evaluation system. A triaxial acceleration sensor was worn on each wrist and chest of the experimental participants. The collected sensor data was used to develop an automatic classification system to distinguish normal

people from HD patients [12,13] proposed a method of using inertial sensors to identify healthy gait, HD patient gait, and hemiplegic patient gait. This method is based on a supervised and trained two-state hidden Markov model, which can be extended to different research subjects for clinical practice and personal health assessment. At the same time, Francisco et al. collected the gait data of HD patients by binding the iPhone on the ankles of patients, using the built-in smart sensor of iPhone, and classified the data with the general assembly (meta) classifier algorithm to distinguish normal people and HD patients [14]. The use of wearable devices to monitor the movements and gaits of HD patients requires the binding of electronic devices to a part of the patient's body, which will affect the patient's movements to a certain extent and cannot collect movement data in the natural state of the patient. At the same time, it will also affect the patient's living comfort. Some scholars have also proposed the idea of using a camera to monitor patient activity, combined with related video image processing algorithms to extract patient activity information. For example, Agrawal et al. proposed a method for human fall detection based on video surveillance, but this may violate the privacy of patients [15].

As far as the authors know, this paper proposes for the first time to use C-band wireless sensing technology to monitor HD patients completely in a non-contact way for continuous monitoring. Compared with the traditional monitoring scheme, it has the following characteristics:


### **3. Principle of Wireless Sensing Technology**

The proposed technical scheme is suitable for non-contact continuous monitoring of HD patients in an indoor environment. Therefore, this section will first describe the indoor model of wireless signal propagation in detail and explain the principles behind wireless sensing technology. Since this article mainly collects CSI data through MSP, we will next reveal the nature of CSI data, derive its mathematical expression, and describe MSP in detail at the end of this section.
