Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version

Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art

Balance differences in people with Parkinson disease with and without freezing of gait

Published in final edited form as: Gait Posture. 2015 September ; 42(3): 306–309. doi:10.1016/j.gaitpost.2015.06.007.BACKGROUND: Freezing of gait (FOG) is a relatively common and remarkably disabling impairment associated with Parkinson disease (PD). Laboratory-based measures indicate that individuals with FOG (PD+FOG) have greater balance deficits than those without FOG (PD-FOG). Whether such differences also can be detected using clinical balance tests has not been investigated. We sought to determine if balance and specific aspects of balance, measured using Balance Evaluation Systems Test (BESTest), differs between PD+FOG and PD-FOG. Furthermore, we aimed to determine if time-efficient clinical balance measures (i.e. Mini-BESTest, Berg Balance Scale (BBS)) could detect balance differences between PD+FOG and PD-FOG. METHODS: Balance of 78 individuals with PD, grouped as either PD+FOG (n=32) or PD-FOG (n=46), was measured using the BESTest, Mini-BESTest, and BBS. Between-groups comparisons were conducted for these measures and for the six sections of the BESTest using analysis of covariance. A PD composite score was used as a covariate. RESULTS: Controlling for motor sign severity, PD duration, and age, PD+FOG had worse balance than PD-FOG when measured using the BESTest (p=0.008, F=7.35) and Mini-BESTest (p=0.002, F=10.37), but not the BBS (p=0.27, F=1.26). BESTest section differences were noted between PD+FOG and PD-FOG for reactive postural responses (p<0.001, F=14.42) and stability in gait (p=0.003, F=9.18). CONCLUSIONS: The BESTest and Mini-BESTest, which specifically assessed reactive postural responses and stability in gait, were more likely than the BBS to detect differences in balance between PD+FOG and PD-FOG. Because it is more time efficient to administer, the Mini-BESTest may be the preferred tool for assessing balance deficits associated with FOG.This study was conducted with funding from the Davis Phinney Foundation, Parkinson's Disease Foundation, NIH R01 NS077959, NIH UL1 TR000448, Greater St. Louis American Parkinson Disease Association (APDA), APDA Center for Advanced PD Research at Washington University in St. Louis. The funding sources had no role in the study design, in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. (Davis Phinney Foundation; Parkinson's Disease Foundation; R01 NS077959 - NIH; UL1 TR000448 - NIH; Greater St. Louis American Parkinson Disease Association (APDA); APDA Center for Advanced PD Research at Washington University in St. Louis

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

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

Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease

Background: Freezing of gait (FOG) is a highly incapacitating symptom that affects many people with Parkinson's disease (PD). Cueing triggered upon real-time FOG detection (on-demand cueing) shows promise for FOG treatment. Yet, the feasibility of implementation and efficacy in daily life is still unknown. Therefore, this study aims to investigate the effectiveness of DeFOG: a smartphone and sensor-based on-demand cueing solution for FOG. Methods: Sixty-two PD patients with FOG will be recruited for this single-blind, multi-center, randomized controlled phase II trial. Patients will be randomized into either the intervention group or the active control group. For four weeks, both groups will receive feedback about their physical activity using the wearable DeFOG system in daily life. In addition, the intervention group will also receive on-demand auditory cueing and instructions. Before and after the intervention, home-based assessments will be performed to evaluate the primary outcome, i.e., “percentage time frozen” during a FOG-provoking protocol. Secondary outcomes include the training effects on physical activity monitored over 7 days and the user-friendliness of the technology. Discussion: The DeFOG trial will investigate the effectiveness of personalized on-demand cueing in a controlled design, delivered for 4 weeks in the patient's home environment. We anticipate that DeFOG will reduce FOG to a greater degree than in the control group and we will explore the impact of the intervention on physical activity levels. We expect to gain in-depth insight into whether and how patients control FOG using cueing methods in their daily lives. Trial registration: Clinicaltrials.gov NCT03978507

Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease

Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson’s disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, &gt; 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD

Gait characterization using wearable inertial sensors in healthy and pathological populations

Gait analysis is emerging as an effective tool to detect an incipient neurodegenerative disease or to monitor its progression. It has been shown that gait disturbances are an early indicator for cognitive impairments and can predict progression to neurodegenerative diseases. Furthermore, gait performance is a predictor of fall status, morbidity and mortality. Instrumented gait analysis provides quantitative measures to support the investigation of gait pathologies and the definition of targeted rehabilitation programs. In this framework, technologies such as inertial sensors are well accepted, and increasingly employed, as tools to characterize locomotion patterns and their variability in research settings. The general aim of this thesis is the evaluation, comparison and refinement of methods for gait characterization using magneto-inertial measurement units (MIMUs), in order to contribute to the migration of instrumented gait analysis from state of the art to state of the science (i.e.: from research towards its application in standard clinical practice). At first, methods for the estimation of spatio-temporal parameters during straight gait were investigated. Such parameters are in fact generally recognized as key metrics for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Although several methods for their estimate have been proposed, few provided a thorough validation. Therefore an error analysis across different pathologies, multiple clinical centers and large sample size was conducted to further validate a previously presented method (TEADRIP). Results confirmed the applicability and robustness of the TEADRIP method. The combination of good performance, reliability and range of usage indicate that the TEADRIP method can be effectively adopted for gait spatio-temporal parameter estimation in the routine clinical practice. However, while traditionally gait analysis is applied to straight walking, several clinical motor tests include turns between straight gait segments. Furthermore, turning is used to evaluate subjects’ motor ability in more challenging circumstances. The second part of the research therefore headed towards the application of gait analysis on turning, both to segment it (i.e.: distinguish turns and straight walking bouts) and to specifically characterize it. Methods for turn identification based on a single MIMU attached to the trunk were implemented and their performance across pathological populations was evaluated. Focusing on Parkinson’s Disease (PD) subjects, turn characterization was also addressed in terms of onset and duration, using MIMUs positioned both on the trunk and on the ankles. Results showed that in PD population turn characterization with the sensors at the ankles lacks of precision, but that a single MIMU positioned on the low back is functional for turn identification. The development and validation of the methods considered in these works allowed for their application to clinical studies, in particular supporting the spatio-temporal parameters analysis in a PD treatment assessment and the investigation of turning characteristic in PD subjects with Freezing of Gait. In the first application, comparing the pre and post parameters it was possible to objectively determine the effectiveness of a rehabilitation treatment. In the second application, quantitative measures confirmed that in PD subjects with Freezing of Gait turning 360° in place is further compromised (and requires additional cognitive effort) compared to turning 180° while walking