An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care

A wearable biofeedback device to improve motor symptoms in Parkinson’s disease

Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the University of Minho. For validation purposes and data acquisition, it was developed a collaboration with the Clinical Academic Center (2CA), located at Braga Hospital. The knowledge acquired in the development of this master thesis is linked to the motor rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also to be used as a clinical decision support tool for the classification of the motor disability level. This system provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease. The system is based on a user- centered design, considering the end-user driven, multitasking and less cognitive effort concepts. This manuscript presents all steps taken along this dissertation regarding: the literature review and respective critical analysis; implemented tech-based procedures; validation outcomes complemented with results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração com Clinical Academic Center (2CA), localizado no Hospital de Braga. Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto, esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS) utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador, considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo. Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica implementados; resultados de validação complementados com discussão de resultados; e principais conclusões e desafios futuros

High-tech aid tool to monitor postural stability in Parkinson’s Disease

Dissertação de mestrado integrado em Engenharia BiomédicaParkinson’s disease (PD) is a neurodegenerative disease that affects around 1% of the population over 65 and has increased in prevalence in recent years. One of the most disabling motor symptoms and a major contributor to falls is postural instability, which threatens the independence and well-being of people with PD. Usually, physicians assess this symptom with a traditional clinical examination named pull test, which, although easy to administer without requiring any instruments, it is a difficult test to standardize and lacks sensitivity to small but significant changes. Thus, other approaches based on high technologies have emerged to provide objective metrics and long-term data on postural stability, complementing clinical assessment. Wearable sensors appeared as a promising tech-based solution to better capture postural instability and eliminate the subjectivity of postural-associated clinical examinations. This dissertation proposes the design, development and validation of a postural assessment tool to perform more objective evaluations of postural instability during basic dynamic day-to-day activities. To achieve this goal, the following steps were accomplished: (i) create a dataset based on 3D motion data of PD patients performing the pull test and dynamic activities using an inertial measurement unit (IMU); (ii) extract relevant features from the data collected, conduct an extensive statistical search, and find correlations to clinical scales; (iii) implement a tool based in artificial intelligence (AI) to classify the level of postural instability through the data collected. Different deep learning models were designed and several combinations of data input were considered in order to find the best model to predict the pull test score. Overall, satisfactory results were achieved as the statistical analysis revealed that many features were considered relevant to distinguish between the scores of the pull test, for diagnostic purposes and also to differentiate the several stages of the disease and levels of motor disability. Regarding the AI-based tool, the results suggest that the combination of IMU-based data with deep learning may be a promising solution for postural instability assessment. The model that achieved the best performance in the testing phase with unseen data presented an accuracy, precision, recall and F1-score of approximately 0.86. The results also show that when fewer daily activities are included in the dataset, the complexity of the model reduces, making it more efficient. Despite the promising results, more data should be collected to assess the actual performance of the model as a postural assessment tool.A doença de Parkinson (DP) é uma doença neurodegenerativa que afeta cerca de 1% da população acima de 65 anos e cuja prevalência tem aumentado nos últimos anos. Um dos sintomas motores mais incapacitantes e um dos principais contribuintes para quedas é a instabilidade postural, que ameaça a independência e o bem-estar das pessoas com a DP. Normalmente, o teste utilizado para avaliar a instabilidade postural é o pull test, que, embora fácil de executar e não necessitando de qualquer instrumento, é um teste difícil de padronizar e com falta de sensibilidade para detetar pequenas alterações que podem ser significativas. Assim, os sensores vestíveis surgiram como uma solução promissora para capturar a instabilidade postural e eliminar a subjetividade dos exames clínicos associados à postura. Esta dissertação tem como objetivo o idealizar, desenvolver e validar um instrumento para realizar avaliações mais objetivas da instabilidade postural durante atividades dinâmicas básicas do dia-a-dia. Para atingir esse objetivo, as seguintes etapas foram realizadas: (i) criar um dataset baseado em dados de movimento 3D de pacientes com a DP enquanto executam o pull test e atividades dinâmicas através de uma unidade de medida inercial; (ii) extrair características relevantes dos dados adquiridos, realizar uma extensa pesquisa estatística e encontrar correlações com escalas clínicas; (iii) implementar uma ferramenta baseada em inteligência artificial (IA) para classificar o nível de instabilidade postural através dos dados recolhidos. É de notar que diferentes frameworks de deep learning foram projetados e vários datasets foram considerados de modo a encontrar o melhor modelo para prever a pontuação da escala do pull test. No geral, os resultados alcançados foram satisfatórios, pois o estudo estatístico revelou que muitas das características extraidas dos sinais recolhidos foram consideradas relevantes para distinguir entre as pontuações do pull test, para fins diagnósticos e também para diferenciar os estágios da doença e os níveis de incapacidade motora. Em relação à ferramenta baseada em IA, os resultados apresentados sugerem que o deep learning pode ser promissor na área de avaliação de instabilidade postural através de IMUs. O modelo que obteve o melhor desempenho apresentou uma exatidão, precisão, sensibilidade e F1-score no teste de aproximadamente 0.86. Os resultados também mostram que dataset com um menor número de actividades diferentes incluídas leva a que o modelo se torne menos complexo, tornando-o mais eficiente. Apesar dos resultados promissores, mais dados devem ser recolhidos para avaliar o real desempenho do modelo como ferramenta de avaliação postural

Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

Wearable Sensor Health Technology (WSHT) captures, analyzes and aggregates physiological data to improve personal well-being. Recently the technology market is flooded with wearable sensors that measure health-related data and have a high user adoption. Nevertheless, these devices are almost exclusively used for fitness purposes and the healthcare sector still faces the challenge of constantly increasing costs. To respond to the necessary but rare use of WSHT in professional healthcare, we aim to identify the most promising areas for future medical implementation. Therefore, we performed a systematic literature search and reviewed 97 papers with regard to disease treatment, application area, vital parameter measurement and target patient. As a result, we could identify five potential areas for further research: (RA1) concentration on widespread diseases, (RA2) expansion of WSHT’s functionality, (RA3) diversity of vital parameter measurements, (RA4) proactive analysis of sensor data for preventive purposes and (RA5) promoting patient adoption through enhanced usability

Quantitative Messung vom Gehen auf der Stelle zur Erhebung von motorischen Symptomen bei Morbus Parkinson

Fluctuating motor symptoms are one of the main challenges in the assessment and evaluation of treatment effects in Parkinson’s disease (PD). The stepping in place task was previously proposed as an assessment of postural control and as surrogate for gait tests, two important evaluations of disturbed motor functions in this disorder. Due to its low spatial requirement, this motor task might specifically be suitable for an instrumental assessment even in remote application. Objective of this study was to explore the quantification of motor features associated with Parkinson's Diseases during stepping in place performance. Methods: Performance of 40 sec stepping in place (SIP) was recorded with a marker-free motion analysis system using a single RGB-Depth camera system. Data from 25 Persons with PD (PwPD, 7 female, Age: mean 65.3 years ± 9.4 years, MDS-UPDRS III 5-65) in up to two different treatment states (OFF: 13, ON: 20) and data from 83 healthy controls (HC, 52 female, Age: 36.8y ± 13.8y) was available for algorithm development, feature extraction and statistical analysis. Based on knee movements, eight spatiotemporal parameters were extracted including cadence, average knee movement amplitude, average and longest step and stance times, asymmetry and arrhythmicity. Parameters were analysed regarding potential confounding effects, technical accuracy and repeatability in HC, their relation to disease severity (MDS-UPDRS III) and postural instability (pull test score) in PwPD and intra- individual differences in treatment states (OFF vs. ON). Results: Six out of eight features showed good accuracy and repeatability in HC subgroup (n=19). Asymmetry and arrhythmicity showed only poor to moderate accuracy (ICC(A,1) > .3; Pearson’s r > .5) and repeatability (ICC(1,1) >.4). No linear confounding effects of age, height and weight were found in HC and PwPD. Decreased knee amplitude was associated with higher disease severity (rho = -.503, p-value = .003) and higher postural instability (rho = - .436, p-value = .014). Knee amplitudes showed also increase of 85.4% from OFF to ON in a subgroup in which recordings were available from both treatment states (n=10). Longer stance time measures were associated with higher disease severity (rho = .523, p-value = .002) and higher postural instability (rho = .468, p-value = .008). 50% of patients with ratings of freezing of gait during MDS-UPDRS III assessment showed freezing during SIP. Conclusion: Instrumental assessment of a 40 sec stepping in place performance may be suitable to quantify common motor symptoms, specifically postural instability, in PwPD. Derived parameters described motor symptoms of PD including decreased ranges of motion (hypokinesia), slower motions (bradykinesia) and increased asymmetry as well as arrhythmicity of stepping movements during SIP. Sensitivity to intra-individual changes, indicates potential use of SIP to monitor fluctuation of motor symptoms in PD.Motorische Fluktuationen sind eine der größten Herausforderungen bei der Beurteilung von Behandlungseffekten bei Morbus Parkinson (PD). Das auf der Stelle Gehen (SIP), wurde ursprünglich als Test zur Haltungskontrolle und als Surrogat für Ganganalyse vorgeschlagen, zwei wichtige Aspekte der gestörten motorischen Funktionen bei Parkinson. Ziel dieser Studie war es, die Quantifizierung von Parkinson-assoziierten motorischen Merkmalen während des Gehens auf der Stelle zu untersuchen. Methoden: Ein makerfreies Bewegungsanalysesystem (RGB-Tiefenkamera) wurde verwendet, um die Ausführung vom SIP über 40 Sekunden aufzuzeichnen. Für die Entwicklung der Algorithmen, die Merkmalsextraktion und die statistische Analyse standen Daten von 25 Personen mit Morbus Parkinson (PwPD, 7 weiblich, Alter: 65,3 Jahre ± 9,4 Jahre, MDS-UPDRS III 5-65) in bis zu zwei verschiedenen Therapiezuständen (OFF: 13, ON: 20) und Daten von 83 gesunden Personen (HC, 52 weiblich, Alter: 36,8 Jahre ± 13,8 Jahre) zur Verfügung. Auf Grundlage der Kniebewegungen wurden acht Parameter extrahiert: Kadenz, durchschnittliche Amplitude der Kniebewegung, durchschnittliche und längste Schritt- und Standzeiten, Asymmetrie und Arrhythmie. Die Parameter wurden im Hinblick auf potenzielle Störfaktoren, technische Genauigkeit und Wiederholbarkeit bei HC, Zusammenhang mit dem Schweregrad der Erkrankung (MDS-UPDRS III) und der posturalen Instabilität (Pull-Test-Score) in PwPD sowie auf intraindividuelle Unterschiede bei den Behandlungszuständen (OFF vs. ON) analysiert. Ergebnisse: Sechs von acht Merkmalen zeigten eine gute Genauigkeit und Wiederholbarkeit in HC (n=19). Asymmetrie und Arrhythmie zeigten nur geringe bis mäßige Genauigkeit (ICC(A,1) > .3; Pearson's r > .5) und Wiederholbarkeit (ICC(1,1) >.4). Bei HC (n=83) und PwPD (n=33) wurden keine linearen Effekte von Alter, Größe und Gewicht festgestellt. Eine verringerte Knieamplitude war mit höherer Krankheitsschwere (rho=-.503, p-Wert = .003) und höherer posturalen Instabilität (rho=-.436, p-Wert=.014) verbunden. Die Knieamplituden nahmen in einer Untergruppe (n=10), von OFF zu ON um 85,4 % zu. Längere Standzeiten waren mit höherer Krankheitsschwere (rho=.523, p-Wert=.002) und höherer posturalen Instabilität (rho=.468, p-Wert=.008) verbunden. 50 % der Patienten, die im MDS-UPDRS-III ein Einfrieren des Gangs zeigten, zeigten auch beim SIP ein Einfrieren. Schlussfolgerung: Die instrumentelle Analyse vom 40-sekündigen Gehen auf der Stelle kann geeignet sein, häufige motorische Symptome, insbesondere posturale Instabilität, bei PwPD zu quantifizieren. Die abgeleiteten Parameter beschrieben die motorischen Symptome von Morbus Parkinson, einschließlich verringerten Bewegungsumfang (Hypokinese), langsamerer Bewegungen (Bradykinese) und Asymmetrie sowie Arrhythmie der Schrittbewegungen. Die Empfindlichkeit gegenüber intraindividuellen Veränderungen deutet auf einen möglichen Einsatz des SIP zum Monitoring motorischer Symptome von PD hin

Objective Assessment of Fall Risk in Parkinson's Disease Using a Body-Fixed Sensor Worn for 3 Days

Background: Patients with Parkinson's disease (PD) suffer from a high fall risk. Previous approaches for evaluating fall risk are based on self-report or testing at a given time point and may, therefore, be insufficient to optimally capture fall risk. We tested, for the first time, whether metrics derived from 3 day continuous recordings are associated with fall risk in PD. Methods and Materials 107 patients (Hoehn & Yahr Stage: 2.6±0.7) wore a small, body-fixed sensor (3D accelerometer) on lower back for 3 days. Walking quantity (e.g., steps per 3-days) and quality (e.g., frequency-derived measures of gait variability) were determined. Subjects were classified as fallers or non-fallers based on fall history. Subjects were also followed for one year to evaluate predictors of the transition from non-faller to faller. Results: The 3 day acceleration derived measures were significantly different in fallers and non-fallers and were significantly correlated with previously validated measures of fall risk. Walking quantity was similar in the two groups. In contrast, the fallers walked with higher step-to-step variability, e.g., anterior-posterior width of the dominant frequency was larger (p = 0.012) in the fallers (0.78±0.17 Hz) compared to the non-fallers (0.71±0.07 Hz). Among subjects who reported no falls in the year prior to testing, sensor-derived measures predicted the time to first fall (p = 0.0034), whereas many traditional measures did not. Cox regression analysis showed that anterior-posterior width was significantly (p = 0.0039) associated with time to fall during the follow-up period, even after adjusting for traditional measures. Conclusions/Significance: These findings indicate that a body-fixed sensor worn continuously can evaluate fall risk in PD. This sensor-based approach was able to identify transition from non-faller to faller, whereas many traditional metrics were not successful. This approach may facilitate earlier detection of fall risk and may in the future, help reduce high costs associated with falls

Visualization of motor symptoms related to Parkinson's disease using wearable sensors

Parkinson Disease (PD) is the second most common neurological disease after Alzheimer. There is a need for long term monitoring to determine with higher accuracy the stage of the disease and regulate the levodopa treatment. Current wearable technology can achieve this monitorization of the patient’s daily life. Motor symptoms of the disease are the most evident and thus, the easiest to target and to relate to the stage of the disease. They are also the ones that suppose the highest impediment for the patients to perform their daily living activities. In this study, motor symptoms are assessed via pressure sensitive insole and gyroscopic sensors placed on the wrists. Eight subjects were analyzed, four controls and four with different stages of PD, performing a 20-step walking test. Pressure sensitive insole showed the transfer of force in the foot during the gait cycle. These signals showed the level of pronation and supination of each step. The force applied against the ground was reduced in subjects with PD, and specially seen in the toe-off phase which translate in a reduction in the ankle force. There was no apparent change in the step time in any of the signals. Gyroscopic data evaluation consisted in time domain, frequency domain and spectrogram analysis and comparing the Root Mean Squared (RMS) value and entropy of the signals with the stages of the disease to see any correlations. These procedures were made with the signals measured in the three axes and with the calculated angular velocity vector module. The analysis showed that the tremor could be visualized and the effects of bradykinesia were visible in the signals while walking. RMS and Entropy values didn’t show significance correlation between the stages of PD and their values with the exception of the RMS values of the signals in the Y axis and of the vector module. Tremors appeared in the frequency domain in the form of peaks at 5 Hz that were constant through the test, as shown by the spectrograms. The frequency domain of the vector module had the same peaks as the rest of the signals but at the double of their frequency. Since all the signals correspond to a different person from simple tests, there was no way of assessing the effects of the different stages of the disease in the same individual over time. Wearable technology supposes a good viable solution to the problem of long-term daily monitoring for patients with PD. Suunto Movesense ® gyroscope sensors and Smart insole Forciot ® suppose a good of non-invasive monitoring technology that can provide long term daily data with minimal discomfort while assessing motor dysfunctions that alter the movements of a patient

Wearable rehabilitation wristband for distal radius fractures

BackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients’ rehabilitation.MethodsA wearable rehabilitation training wristband was proposed, which could be used in the patient’s daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.ResultsExperimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.ConclusionThe developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings