Contactless finger tapping detection at C band

The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice

Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson Disease

Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease

Using wearable sensor systems for objective assessment of parkinson's disease

This paper presents a novel wearable sensor system based on the integration of miniaturised IMUs for fine hand movement analysis. The system, named SensHand V1, is composed of full 9-axis inertial sensors, placed on the fingers and wrist, which are managed by a cortex-M3 microcontroller. The acquired data are sent to a data logger through the use of Bluetooth communication. In this paper, the system is used for the objective diagnosis of Parkinson's disease, which is commonly assessed by neurologists through visual examination of motor tasks and semi-quantitative rating scales. Here, these motor tasks are also assessed using the SensHand V1, and then compared with the subjective metrics. Results demonstrate that the system is adequate to support neurologists in diagnostic procedures and allows for an objective evaluation of the disease

Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy

A Method for Automatic and Objective Scoring of Bradykinesia Using Orientation Sensors and Classification Algorithms

Correct assessment of bradykinesia is a key element in the diagnosis and monitoring of Parkinson's disease. Its evaluation is based on a careful assessment of symptoms and it is quantified using rating scales, where the Movement Disorders Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard. Regardless of their importance, the bradykinesia-related items show low agreement between different evaluators. In this study, we design an applicable tool that provides an objective quantification of bradykinesia and that evaluates all characteristics described in the MDS-UPDRS. Twenty-five patients with Parkinson's disease performed three of the five bradykinesia-related items of the MDS-UPDRS. Their movements were assessed by four evaluators and were recorded with a nine degrees-of-freedom sensor. Sensor fusion was employed to obtain a 3-D representation of movements. Based on the resulting signals, a set of features related to the characteristics described in the MDS-UPDRS was defined. Feature selection methods were employed to determine the most important features to quantify bradykinesia. The features selected were used to train support vector machine classifiers to obtain an automatic score of the movements of each patient. The best results were obtained when seven features were included in the classifiers. The classification errors for finger tapping, diadochokinesis and toe tapping were 15-16.5%, 9.3-9.8%, and 18.2-20.2% smaller than the average interrater scoring error, respectively. The introduction of objective scoring in the assessment of bradykinesia might eliminate inconsistencies within evaluators and interrater assessment disagreements and might improve the monitoring of movement disorders

Motor symptoms in Parkinson's disease: A unified framework

Parkinson’s disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement

Quantitative Analyse des Fuß-Tappings bei Risikopersonen für sowie bei Patienten mit idiopathischem Parkinsonsyndrom: Assoziation mit axialen Symptomen und Lateralität

Eine lange prodromale Phase ist typisch für das IPS, das zu den häufigsten neurologischen Erkrankungen der Welt gehört. Die klinische Diagnose kann durch typische motorische Symptomatik oftmals erst gestellt werden, wenn 50-60% der dopaminergen Neurone der Substantia nigra zerstört sind. Besonders im Hinblick auf die mögliche Entwicklung neuroprotektiver Substanzen in der Zukunft nimmt die Bedeutung einer frühzeitigen Diagnose zur günstigen Beeinflussung des Krankheitsverlaufs immer weiter zu. Vor allem REM-Schlafstörung, Hyposmie und Depression / depressive Phasen zeigen einen Zusammenhang mit gehäuftem Auftreten eines IPS und können deshalb als Prodromalmarker gelten. In dieser Arbeit wurde mittels eines hochsensitiven Drucksensor Q-Motor (MINI-40, ATI Industrial Automation, Apex, NC, USA) die Feinmotorik der unteren Extremität mittels Fuß-Tapping untersucht. Hierfür wurden die Kohorten mit Prodromalmarkern untereinander und mit der Kontroll-Kohorte sowie der IPS-Kohorte verglichen. Die Probanden wurden aufgefordert, so schnell und so gleichmäßig wie möglich zu tappen. Ausgewertet wurden jeweils Parameter für Geschwindigkeit: IPI mean (die Zeit zwischen den jeweils größten Ausschlägen als Marker für die Schnelligkeit), IPI SD (die Standardabweichung der Zeit zwischen den jeweils größten Ausschlägen und somit ein Marker für die Rhythmizität) und Parameter für Kraft: TF mean (die Kraft, die für den jeweiligen Tap aufgewendet wurde) und TF CoV (der Änderungskoeffizient der aufgebrachten Kraft (TF) und somit ein Marker für die Gleichmäßigkeit des Kraftaufwands). Eine signifikant langsamere Tapping-Geschwindigkeit der IPS-Kohorte oder bei Kohorten mit Prodromalmarkern, möglicherweise hinweisend auf eine (subtile) Akinese, konnte in dieser Arbeit nicht dargestellt werden. Auch ergaben sich keine Auffälligkeiten in Bezug auf die aufgewendete Kraft der Bewegung. Im Gegensatz dazu lieferten die Berechnungen interessante Daten bezüglich der Rhythmizität des Tappings (IPI SD). Vorrangig die Hyposmie-Kohorte und die Hyp/RBD-Kohorten tappten signifikant unregelmäßiger als die Kontroll-Kohorte. Innerhalb der Lateralitätsuntersuchungen konnte bei der Hyp/RBD-Kohorte ein höherer Seitenunterschied in der Schnelligkeit und auch der Gleichmäßigkeit des Tappens festgestellt werden. Somit zeigte sowohl die Hyposmie-Kohorte, als auch die Hyp/RBD-Kohorte die größten Auffälligkeiten in der Feinmotorik der unteren Extremität und einen größeren Seitenunterschied im Vergleich zu Kontrollen. Es ist davon auszugehen, dass nicht nur die Anzahl der Prodromalmarker, sondern auch die spezifische Art der Prodromalmarker (hier v.a. Hyposmie und RBD) von Bedeutung ist. In zukünftigen Studien sollte daher ein besonderes Augenmerk auf Personen mit Hyposmie und RBD gelegt werden. Sie würden sich möglicherweise im Rahmen von Prodromal-Kohorten für eine Erfassung von Progression mittels Fuß-Tapping in diesem Stadium eignen. Auch scheint die Variabilität in Bewegungsabläufen ein vielversprechender Marker für weitere Untersuchungen in Kohorten mit Prodromalmarkern zu sein. Es konnte eine positive Korrelation zwischen der Rhythmizität der Bewegung (IPI SD) des Fuß-Tappings und des MDS-UPDRS III Scores festgestellt werden, was für die Validität des Parameters IPI SD des Q-Motor Testsystems spricht. Des Weiteren korrelierte die Geschwindigkeit (IPI mean) des Fuß-Tappings mit dem axial motor subscore. Fuß-Tapping scheint somit nicht nur die Feinmotorik der unteren Extremität (und damit eine mögliche Akinese) abzubilden, sondern auch mit axialer Motorik assoziiert zu sein. Mittels Q-Motor Testsystem und Erfassung des Fuß-Tappings konnten in dieser Arbeit somit Ansätze für weitere Untersuchungen mit bestimmten Kohorten und potentiell interessante Parameter definiert werden. Interessanterweise fanden sich keine signifikanten Unterschiede zwischen der IPS-Kohorte und der Kontroll-Kohorte, wobei dies a. e. auf die stark unterschiedlichen Kohortengrößen zurückzuführen ist. Aufgrund des longitudinalen Designs der Studie (über 20 Jahre werden die Probanden alle zwei Jahre untersucht) und der alternden Probanden können sich die Kohortengrößen zukünftig angleichen und weitere interessante Daten liefern

Avaliação de parâmetros temporais e inerciais para a caracterização da bradicinesia parkinsoniana durante a extensão do punho

Many researchers have been characterizing bradykinesia manifested in Parkinson’s disease using a subjective scale. Recently, some researchers proposed the use of inertial sensors and electromyography to identify bradykinesia symptom in Parkinson’s patient. One of the main problems found in these studies is related to the identification of the parameters extracted from the signals, wich allows the recognition of patterns that discriminates normality from bradykinesia symptom. In this context, this research investigated the pattern of 8 temporal and inertial parameters in order to characterize healthy subjects and subjects with Parkinson’s disease. The temporal parameters were: time to achieve maximum angular velocity (TVangMax), linear acceleration (TAccMax), variation in the magnetic field (TMagMax), muscle contraction (TEMGMax) and total time to perform the task (TTASK). The inertial parameters were: maximum angular velocity (VAngMax), maximum acceleration (AccMax) and maximum variation in the magnetic field (MagMax). A total of 27 subjects participated in this research, 12 healthy subjects (60.1 ± 6.1 years) and 15 with Parkinson’s disease (65,3 ± 9,1 years). A neurologist evaluated how severe was the subject’s Parkinson’s disease using a UPDRS scale. All individuals performed a supported forearm wrist extension. The movements were performed with and without a load of 92 grams. The Wilconox test was used to make a comparative evaluation of the parameters resulting in a p < 0.05. The results of this research show the load does not have any influence on the estimated parameters, excepted the MagMax, which enabled the discrimination between groups. These results are relevant for clinical studies that need these parameters to assess and monitor the efficiency of therapies, related to the control of the bradykinesia symptom in Parkinson’s disease.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)A caracterização da bradicinesia parkinsoniana tem sido foco de vários trabalhos e a maioria desses estudos utiliza escalas subjetivas para a sua avaliação. Recentemente, algumas pesquisas têm proposto a avaliação da bradicinesia por meio de sensores inerciais e eletromiográficos. Uma das principais dificuldades na realização desses estudos é relacionada à identificação de parâmetros extraídos dos sinais mensurados, que possibilitem a caracterização de padrões de normalidade e bradicinesia. Neste sentido, essa pesquisa investigou o padrão de oito parâmetros temporais e inerciais, com a finalidade de caracterizar grupos de indivíduos hígidos e com a doença de Parkinson, sendo esses, cinco temporais – tempo para atingir a máxima velocidade angular (TVangMax), aceleração linear (TAccMax), variação do campo magnético (TMagMax), contração muscular (TEMGMax) e o tempo total de execução da tarefa (TTASK); e três inerciais - velocidade angular máxima (VAngMax), aceleração máxima (AccMax) e variação de campo magnético máxima (MagMax). No total 27 indivíduos participaram do estudo, sendo 12 hígidos (60,1 ± 6,1 anos) e 15 com a doença de Parkinson (65,3 ± 9,1 anos). A severidade da bradicinesia dos indivíduos com a doença de Parkinson foi avaliada pelo neurologista com base na escala UPDRS. Todos os indivíduos executaram o movimento de extensão de punho antigravitacional com o antebraço apoiado em pronação. Os movimentos foram executados com e sem a presença de uma carga de 92 gramas posicionada no dorso da mão. A avaliação comparativa entre os parâmetros estimados para cada grupo baseou-se na aplicação do teste Wilcoxon com p < 0,05. O teste foi aplicado tanto inter quanto intra grupos, para verificar quais as características são significativas para discriminar pacientes com e sem a doença de Parkinson (inter) e a comparação intra grupos verificou-se a influência da carga na bradicinesia. Os resultados mostraram que todos os parâmetros estimados, com exceção do MagMax, possibilitaram a discriminação entre os grupos. Eles mostram ainda que a carga utilizada não influenciou na bradicinesia. Esses achados são relevantes para estudos clínicos que necessitam de parâmetros objetivos para a avaliação e monitoramento da eficácia de terapias relacionadas ao controle da bradicinesia parkinsoniana