Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection

Enhancing the measurement of clinical outcomes using Microsoft Kinect

There is a growing body of applications leveraging Microsoft Kinect and the associated Windows Software Development Kit in health and wellness. In particular, this platform has been valuable in developing interactive solutions for rehabilitation including creating more engaging exercise regimens and ensuring that exercises are performed correctly for optimal outcomes. Clinical trials rely upon robust and validated methodologies to measure health status and to detect treatment-related changes over time to enable the efficacy and safety of new drug treatments to be assessed and measured. In many therapeutic areas, traditional outcome measures rely on subjective investigator and patient ratings. Subjective ratings are not always sensitive to detecting small improvements, are subject to inter- and intra-rater variability and limited in their ability to record detailed or subtle aspects of movement and mobility. For these reasons, objective measurements may provide greater sensitivity to detect treatment-related changes where they exist. In this review paper, we explore the use of the Kinect platform to develop low-cost approaches to objectively measure aspects of movement. We consider published applications that measure aspects of gait and balance, upper extremity movement, chest wall motion and facial analysis. In each case, we explore the utility of the approach for clinical trials, and the precision and accuracy of estimates derived from the Kinect output. We conclude that the use of games platforms such as Microsoft Kinect to measure clinical outcomes offer a versatile, easy to use and low-cost approach that may add significant value and utility to clinical drug development, in particular in replacing conventional subjective measures and providing richer information about movement than previously possible in large scale clinical trials, especially in the measurement of gross spatial movements. Regulatory acceptance of clinical outcomes collected in this way will be subject to comprehensive assessment of validity and clinical relevance, and this will require good quality peer-reviewed publications of scientific evidence

Sistema para análise automatizada de movimento durante a marcha usando uma câmara RGB-D

Nowadays it is still common in clinical practice to assess the gait (or way of walking) of a given subject through the visual observation and use of a rating scale, which is a subjective approach. However, sensors including RGB-D cameras, such as the Microsoft Kinect, can be used to obtain quantitative information that allows performing gait analysis in a more objective way. The quantitative gait analysis results can be very useful for example to support the clinical assessment of patients with diseases that can affect their gait, such as Parkinson’s disease. The main motivation of this thesis was thus to provide support to gait assessment, by allowing to carry out quantitative gait analysis in an automated way. This objective was achieved by using 3-D data, provided by a single RGB-D camera, to automatically select the data corresponding to walking and then detect the gait cycles performed by the subject while walking. For each detected gait cycle, we obtain several gait parameters, which are used together with anthropometric measures to automatically identify the subject being assessed. The automated gait data selection relies on machine learning techniques to recognize three different activities (walking, standing, and marching), as well as two different positions of the subject in relation to the camera (facing the camera and facing away from it). For gait cycle detection, we developed an algorithm that estimates the instants corresponding to given gait events. The subject identification based on gait is enabled by a solution that was also developed by relying on machine learning. The developed solutions were integrated into a system for automated gait analysis, which we found to be a viable alternative to gold standard systems for obtaining several spatiotemporal and some kinematic gait parameters. Furthermore, the system is suitable for use in clinical environments, as well as ambulatory scenarios, since it relies on a single markerless RGB-D camera that is less expensive, more portable, less intrusive and easier to set up, when compared with the gold standard systems (multiple cameras and several markers attached to the subject’s body).Atualmente ainda é comum na prática clínica avaliar a marcha (ou o modo de andar) de uma certa pessoa através da observação visual e utilização de uma escala de classificação, o que é uma abordagem subjetiva. No entanto, existem sensores incluindo câmaras RGB-D, como a Microsoft Kinect, que podem ser usados para obter informação quantitativa que permite realizar a análise da marcha de um modo mais objetivo. Os resultados quantitativos da análise da marcha podem ser muito úteis, por exemplo, para apoiar a avaliação clínica de pessoas com doenças que podem afetar a sua marcha, como a doença de Parkinson. Assim, a principal motivação desta tese foi fornecer apoio à avaliação da marcha, permitindo realizar a análise quantitativa da marcha de forma automatizada. Este objetivo foi atingido usando dados em 3-D, fornecidos por uma única câmara RGB-D, para automaticamente selecionar os dados correspondentes a andar e, em seguida, detetar os ciclos de marcha executados pelo sujeito durante a marcha. Para cada ciclo de marcha identificado, obtemos vários parâmetros de marcha, que são usados em conjunto com medidas antropométricas para identificar automaticamente o sujeito que está a ser avaliado. A seleção automatizada de dados de marcha usa técnicas de aprendizagem máquina para reconhecer três atividades diferentes (andar, estar parado em pé e marchar), bem como duas posições diferentes do sujeito em relação à câmara (de frente para a câmara e de costas para ela). Para a deteção dos ciclos da marcha, desenvolvemos um algoritmo que estima os instantes correspondentes a determinados eventos da marcha. A identificação do sujeito com base na sua marcha é realizada usando uma solução que também foi desenvolvida com base em aprendizagem máquina. As soluções desenvolvidas foram integradas num sistema de análise automatizada de marcha, que demonstrámos ser uma alternativa viável a sistemas padrão de referência para obter vários parâmetros de marcha espácio-temporais e alguns parâmetros angulares. Além disso, o sistema é adequado para uso em ambientes clínicos, bem como em cenários ambulatórios, pois depende de apenas de uma câmara RGB-D que não usa marcadores e é menos dispendiosa, mais portátil, menos intrusiva e mais fácil de configurar, quando comparada com os sistemas padrão de referência (múltiplas câmaras e vários marcadores colocados no corpo do sujeito).Programa Doutoral em Informátic

Using 3D sensing and projecting technology to improve the mobility of Parkinson's disease patients

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonParkinson’s is a neurological condition in which parts of the brain responsible for movements becomes incapacitated over time due to the abnormal dopamine equilibrium. Freezing of Gait (FOG) is one of the main Parkinson’s Disease (PD) symptoms that affects patients not only physically but also psychologically as it prevents them from fulfilling simple tasks such as standing up or walking. Different auditory and visual cues have been proven to be very effective in improving the mobility of People with Parkinson’s (PwP). Nonetheless, many of the available methods require user intervention or devices to be worn, charged, etc. to activate the cues. This research suggests a system that can provide an unobtrusive facility to detect FOG and falling in PwP as well as monitoring and improving their mobility using laser-based visual cues casted by an automated laser system. It proposes a new indoor method for casting a set of two parallel laser lines as a dynamic visual cue in front of a subject’s feet based on the subject’s head direction and 3D location in a room. The proposed system controls the movement of a set of pan/tilt servo motors and laser pointers using a microcontroller based on the real-time skeletal information acquired from a Kinect v2 sensor. A Graphical User Interface (GUI) is created that enables users to control and adjust the settings based on the user preferences. The system was tested and trained by 12 healthy participants and reviewed by 15 PwP who suffer from frequent FOG episodes. The results showed the possibility of employing the system as an indoor and on-demand visual cue system for PwP that does not rely on the subject’s input or introduce any additional complexities to operate. Despite limitations regarding its outdoor use, feedback was very positive in terms of domestic usability and convenience, where 12/15 PwP showed interest in installing and using the system at their homes

Proceedings XXIII Congresso SIAMOC 2023

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione

Deep Learning Based Abnormal Gait Classification System Study with Heterogeneous Sensor Network

Gait is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion site and has been demonstrated to play a guiding role in clinical research such as medical diagnosis and disease prevention. In order to promote the research of automatic gait pattern recognition, this paper introduces the research status of abnormal gait recognition and systems analysis of the common gait recognition technologies. Based on this, two gait information extraction methods, sensor-based and vision-based, are studied, including wearable system design and deep neural network-based algorithm design. In the sensor-based study, we proposed a lower limb data acquisition system. The experiment was designed to collect acceleration signals and sEMG signals under normal and pathological gaits. Specifically, wearable hardware-based on MSP430 and upper computer software based on Labview is designed. The hardware system consists of EMG foot ring, high-precision IMU and pressure-sensitive intelligent insole. Data of 15 healthy persons and 15 hemiplegic patients during walking were collected. The classification of gait was carried out based on sEMG and the average accuracy rate can reach 92.8% for CNN. For IMU signals five kinds of abnormal gait are trained based on three models: BPNN, LSTM, and CNN. The experimental results show that the system combined with the neural network can classify different pathological gaits well, and the average accuracy rate of the six-classifications task can reach 93%. In vision-based research, by using human keypoint detection technology, we obtain the precise location of the key points through the fusion of thermal mapping and offset, thus extracts the space-time information of the key points. However, the results show that even the state-of-the-art is not good enough for replacing IMU in gait analysis and classification. The good news is the rhythm wave can be observed within 2 m, which proves that the temporal and spatial information of the key points extracted is highly correlated with the acceleration information collected by IMU, which paved the way for the visual-based abnormal gait classification algorithm.步态指人走路时表现出来的姿态，是人体重要生物特征之一。异常步态多与病变部位有关，作为反映人体健康状况和行为能力的重要特征，其被论证在医疗诊断、疾病预防等临床研究中具有指导作用。为了促进步态模式自动识别的研究，本文介绍了异常步态识别的研究现状，系统地分析了常见步态识别技术以及算法，以此为基础研究了基于传感器与基于视觉两种步态信息提取方法，内容包括可穿戴系统设计与基于深度神经网络的算法设计。 在基于传感器的研究中，本工作开发了下肢步态信息采集系统，并利用该信息采集系统设计实验，采集正常与不同病理步态下的加速度信号与肌电信号，搭建深度神经网络完成分类任务。具体的，在系统搭建部分设计了基于MSP430的可穿戴硬件设备以及基于Labview的上位机软件，该硬件系统由肌电脚环，高精度IMU以及压感智能鞋垫组成，该上位机软件接收、解包蓝牙数据并计算出步频步长等常用步态参数。 在基于运动信号与基于表面肌电的研究中，采集了15名健康人与15名偏瘫病人的步态数据，并针对表面肌电信号训练卷积神经网络进行帕金森步态的识别与分类，平均准确率可达92.8%。针对运动信号训练了反向传播神经网络，LSTM以及卷积神经网络三种模型进行五种异常步态的分类任务。实验结果表明，本工作中步态信息采集系统结合神经网络模型，可以很好地对不同病理步态进行分类，六分类平均正确率可达93%。 在基于视觉的研究中，本文利用人体关键点检测技术，首先检测出图片中的一个或多个人，接着对边界框做图像分割，接着采用全卷积resnet对每一个边界框中的人物的主要关节点做热力图并分析偏移量，最后通过热力图与偏移的融合得到关键点的精确定位。通过该算法提取了不同步态下姿态关键点时空信息，为基于视觉的步态分析系统提供了基础条件。但实验结果表明目前最高准确率的人体关键点检测算法不足以替代IMU实现步态分析与分类。但在2m之内可以观察到节律信息，证明了所提取的关键点时空信息与IMU采集的加速度信息呈现较高相关度，为基于视觉的异常步态分类算法铺平了道路

An Internet- and Kinect-Based Multiple Sclerosis Fitness Intervention Training With Pilates Exercises: Development and Usability Study

background: balance impairments are common in people with multiple sclerosis (MS), with reduced ability to maintain position and delayed responses to postural adjustments. Pilates is a popular alternative method for balance training that may reduce the rapid worsening of symptoms and the increased risk of secondary conditions (eg, depression) that are frequently associated with physical inactivity.objective: In this paper, we aimed to describe the design, development, and usability testing of MS Fitness Intervention Training (MS-FIT), a Kinect-based tool implementing Pilates exercises customized for MS. methods: MS-FIT has been developed using a user-centered design approach (design, prototype, user feedback, and analysis) to gain the target user's perspective. a team composed of 1 physical therapist, 2 game programmers, and 1 game designer developed the first version of MS-FIT that integrated the knowledge and experience of the team with MS literature findings related to pilates exercises and balance interventions based on exergames. MS-FIT, developed by using the Unity 3D (Unity Technologies) game engine software with kinect Sensor V2 for Windows, implements exercises for breathing, posture, and balance. Feedback from an Italian panel of experts in MS rehabilitation (neurologists, physiatrists, physical therapists, 1 statistician, and 1 bioengineer) and people with MS was collected to customize the tool for use in MS. The context of MS-FIT is traveling around the world to visit some of the most important cities to learn the aspects of their culture through pictures and stories. At each stay of the travel, the avatar of a Pilates teacher shows the user the exercises to be performed. Overall, 9 people with MS (n=4, 44% women; mean age 42.89, SD 11.97 years; mean disease duration 10.19, SD 9.18 years; Expanded Disability Status Scale score 3.17, SD 0.75) were involved in 3 outpatient user test sessions of 30 minutes; MS-FIT's usability was assessed through an ad hoc questionnaire (maximum value=5; higher the score, higher the usability) evaluating easiness to use, playability, enjoyment, satisfaction, and acceptance.Results: A user-centered design approach was used to develop an accessible and challenging tool for balance training. all people with MS (9/9, 100%) completed the user test sessions and answered the ad hoc questionnaire. the average score on each item ranged from 3.78 (SD 0.67) to 4.33 (SD 1.00), which indicated a high usability level. The feedback and suggestions provided by 64% (9/14) of people with MS and 36% (5/14) of therapists involved in the user test were implemented to refine the first prototype to release MS-FIT 2.0. Conclusions: The participants reported that MS-FIT was a usable tool. It is a promising system for enhancing the motivation and engagement of people with MS in performing exercise with the aim of improving their physical status

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention

Non-contact measures to monitor hand movement of people with rheumatoid arthritis using a monocular RGB camera

Hand movements play an essential role in a person’s ability to interact with the environment. In hand biomechanics, the range of joint motion is a crucial metric to quantify changes due to degenerative pathologies, such as rheumatoid arthritis (RA). RA is a chronic condition where the immune system mistakenly attacks the joints, particularly those in the hands. Optoelectronic motion capture systems are gold-standard tools to quantify changes but are challenging to adopt outside laboratory settings. Deep learning executed on standard video data can capture RA participants in their natural environments, potentially supporting objectivity in remote consultation. The three main research aims in this thesis were 1) to assess the extent to which current deep learning architectures, which have been validated for quantifying motion of other body segments, can be applied to hand kinematics using monocular RGB cameras, 2) to localise where in videos the hand motions of interest are to be found, 3) to assess the validity of 1) and 2) to determine disease status in RA. First, hand kinematics for twelve healthy participants, captured with OpenPose were benchmarked against those captured using an optoelectronic system, showing acceptable instrument errors below 10°. Then, a gesture classifier was tested to segment video recordings of twenty-two healthy participants, achieving an accuracy of 93.5%. Finally, OpenPose and the classifier were applied to videos of RA participants performing hand exercises to determine disease status. The inferred disease activity exhibited agreement with the in-person ground truth in nine out of ten instances, outperforming virtual consultations, which agreed only six times out of ten. These results demonstrate that this approach is more effective than estimated disease activity performed by human experts during video consultations. The end goal sets the foundation for a tool that RA participants can use to observe their disease activity from their home.Open Acces