Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective: We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods: Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results: Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions: We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.info:eu-repo/semantics/publishedVersio

Deep learning for Parkinson's disease: a case study on Freezing of Gait

We propose a deep-learning method for feature extraction from gait data of Parkinson’s disease patients. Our goal is to verify whether a fine classification of gait between similar groups can be achieved. To this end, we refer as a case study to the Freezing of Gait (FOG), and we measure gait data from two groups of patients, which exhibit (respectively, do not exhibit) this symptom. Wearable inertial sensors are employed, and data are collected during activities similar to those performed by patients during their daily living. Moreover, most patients are in daily on state, hence the two groups are difficult to classify, as their gait does not exhibit evident differences. Whereas classical Machine Learning methods are not sufficiently robust to perform such a fine classification, if they are fed with features extracted by means of a deep network, the results are satisfactory also when a large dataset is not available and data present a mild degree of heterogeneit

Towards a wearable system for predicting the freezing of gait in people affected by Parkinson's disease

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the march. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG

Machine Learning in Tremor Analysis: Critique and Directions

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Prediction and Detection of Freezing of Gait in Parkinson's Disease using Plantar Pressure Data

Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting movement and is characterized by symptoms such as tremor, rigidity, and Freezing of Gait (FOG). FOG is a walking disturbance seen in more advanced stages of PD. FOG is characterized by the feeling of feet being glued to the ground and has been associated with higher risks of falls. While falling can have great repercussions in individuals with PD, leading to restricted movement and independence, hip fracture, and fatal injury, even the disturbance of FOG alone can lead to decreased mobility, inactivity, and decreased quality of life. Determining methods to counter FOG can potentially lead to a better life for people with PD (PwPD). Freezing episodes can be countered with the help of external intervention such as visual or auditory cues. Such intervention when administered during the freeze has been found to alleviate the freeze and thus prevent freeze-related falls. This sheds the importance of detecting or predicting a freeze event. Once a freeze is detected or predicted, an intervention can be administered to help prevent the freeze altogether (in case of prediction) or help resume normal walking (in case of detection). Different wearable sensors have been used to collect data from participants to understand FOG and develop approaches to detect and predict it. Plantar pressure data has earlier been used in gait related studies; however, they have not been used for FOG detection or prediction. Based on the hypothesis that plantar pressure data can capture subtle weight shifts unique to FOG episodes, this research aimed to determine if plantar pressure data alone can be used to detect and predict FOG. In this research, plantar (foot sole) pressure data were collected from shoe-insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path while on their normal antiparkinsonian medication. The sensors included IMU, EMG, and plantar pressure foot insoles; however, for the research in this thesis, only plantar pressure data were used. The walking trials were also video recorded for labelling the data. A custom-built application was used to synchronize data from all sensors and label them. This was followed by feature extraction, dataset balancing, and z-score normalization. The datasets generated were then classified using Long-short term memory (LSTM) networks. The best model had an average 82.0% (SD 6.25%) sensitivity and 89.4% (SD 3.60%) specificity for one-freezer-held-out cross validation tests. For the participants who did not freeze during the walking trials, an average 87.7% specificity was achieved. Since, FOG detection is done with the aim to provide an intervention, a freeze episode analysis was completed, and it was found that the model could correctly detect 95% of freeze episodes. The misclassified freezes and false positives were analyzed with respect to active (walking and turning) and inactive states (standing). The model’s specificity performance for one-freezer held out cross validation tests was found to improve to 93.3% when analyzing the model only on active states. FOG prediction was done afterwards, including data before FOG (labelled Pre-FOG) in the target class. The best FOG prediction method achieved an average 74.02% (SD 12.48%) sensitivity and 82.99% (SD 5.75%) specificity for one-freezer-held-out cross validation tests. The research showed that plantar pressure data can be successfully used for FOG detection and prediction. Moving away from window-based model also helped the research in reducing the freeze detection latency. However, further research is required to improve the FOG prediction performance and a bigger sample size should be used in future research

An enhanced sensor-based approach for evaluation of a geriatric fall risk in non-ambulatory environments

Jedes Jahr stürzt rund ein Drittel der über 65 Jährigen. Stürze sind die Hauptursache für mittlere bis schwere Verletzungen und damit eine enorme Belastung für das Gesundheitssystem. Eine zeitlich akkurate Sturzrisikobewertung in einer breit akzeptierten und nicht-stigmatisierenden Art und Weise kann zu signifikanten Veränderungen in der Strategie der Sturzprävention führen und damit dazu beitragen, die Anzahl der stürzenden Personen, sowie die Sturzrate zu reduzieren. Die gegenwärtige klinische Evaluierung des Sturzrisikos ist zeitaufwendig und subjektiv. Folglich sind Bewertungen in stationärem Umfeld obstruktiv, oder fokussieren sich ausschließlich auf einmalige, periodische Merkmale der menschlichen Bewegung. Der Fokus dieser Arbeit liegt in der Erforschung und Definition neuer Konzepte zur Beurteilung der Koordination der Extremitäten, der Art des Gehens und der Aufstehvorgänge anhand von Signalen von am Handgelenk getragener Inertial- und Umgebungssensorik. Merkmale im Zeit- und Frequenzraum wurden händisch entwickelt, um daraus Support Vector Maschine -Modelle abzuleiten. Die Modelle beschreiben die physikalische Leistungsfähigkeit einer Person in Form einer objektiven (quantitativen) Sturzrisikobewertung in einem störungsanfälligen häuslichen Umfeld. Für erste Untersuchungszwecke wurde eine Forschungsstudie mit 28 älteren Teilnehmern in einem kontrollierten Umfeld durchgeführt. Darauf aufsetzend wurde eine große Querschnittsstudie mit einer Kohorte von 180 Probanden durchgeführt. Eine sich der Messwoche anschließende sechsmonatige Nachverfolgungsphase wurde zur Validierung der Modelle in die Studie inkludiert. Die Ergebnisse haben einen neuen Prädiktor für akutes Sturzrisiko hervorgebracht. Zusätzlich konnte aufgezeigt werden, dass die Kenntnis der Umgebungsbedingungen relevant sind, um die menschlichen Bewegungen richtig bewerten zu können. Ein innovativer Echtzeitalgorithmus wurde entwickelt, in dem Multi-Sensor-Ansätze fusioniert, sowie auf Bewegung basierende Filter integriert sind. Die Einflüsse der Hand-Abhängigkeit auf die Leistungsfähigkeit des Algorithmus konnten im Rahmen dieser Arbeit untersucht werden. Die Validierung der entwickelten Modelle in allen drei Domänen gegen die Grundwahrheit zeigt eine klinisch relevante Genauigkeit oder zumindest teilweise bessere Ergebnisse gegenüber dem Stand der Technik. Die Studie zeigt die Möglichkeit auf, Einschränkungen klinischer Tests zu bewältigen, sowie in Armbändern integrierte Sensorik sowohl für eine akute, wie auch eine konventionelle Sechsmontasbewertung des Sturzrisikos verlässlich anzuwenden

A roadmap to inform development, validation and approval of digital mobility outcomes: the Mobilise-D approach

Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care