Laserlight visual cueing device for freezing of gait in Parkinson's disease: a case study of the biomechanics involved

AbstractBackground: Freezing of gait (FOG) is a serious gait disorder affecting up to two-thirds of people with Parkinson's disease (PD). Cueing has been explored as a method of generating motor execution using visual transverse lines on the floor. However, the impact of a laser light visual cue remains unclear. Objective: To determine the biomechanical effect of a laser cane on FOG in a participant with PD compared to a healthy age- and gender-matched control. Methods: The participant with PD and healthy control were given a task of initiating gait from standing. Electromyography (EMG) data were collected from the tibialis anterior (TA) and the medial gastrocnemius (GS) muscles using an 8-channel system. A 10-camera system (Qualisys) recorded movement in 6 degrees of freedom and a calibrated anatomical system technique was used to construct a full body model. Center of mass (COM) and center of pressure (COP) were the main outcome measures. Results: The uncued condition showed that separation of COM and COP took longer and was of smaller magnitude than the cued condition. EMG activity revealed prolonged activation of GS, with little to no TA activity. The cued condition showed earlier COM and COP separation. There was reduced fluctuation in GS, with abnormal, early bursts of TA activity. Step length improved in the cued condition compared to the uncued condition. Conclusion: Laserlight visual cueing improved step length beyond a non-cued condition for this patient indicating improved posture and muscle control

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

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

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, > 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

Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices

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

Fall risk factors and exercise in Parkinson's disease

Many people with Parkinson’s disease fall frequently and recurrently. The consequences of falls can be devastating, far reaching and costly. Unfortunately, medications for Parkinson’s disease do not appear to prevent falls. The overall aim of the studies in this thesis was to evaluate and explore exercise interventions with the potential to reduce fall risk in people with Parkinson’s disease. Leg muscle weakness, freezing of gait and reduced balance are risk factors for falls which are potentially remediable with exercise in people with Parkinson’s disease. However, there is a paucity of research into the effects of exercise training on fall risk in this group. A randomised controlled trial with blinded assessment was conducted to assess the effect on fall risk of a six-month exercise program which targeted these risk factors compared with usual care in people with Parkinson’s disease. Forty-eight participants with Parkinson’s disease who had fallen or were at risk of falling were randomised to the exercise or control group. The exercise group attended a monthly exercise class and performed exercises at home, such that exercises were performed three times per week. Both groups received falls prevention advice. The primary outcome measure was a Parkinson’s disease fall risk score (% risk of falling) – an algorithm consisting of weighted contributions from knee extensor muscle strength of the weaker leg, balance in standing and freezing of gait. Secondary outcome measures included measures of the targeted risk factors as well as physical abilities, fear of falling and quality of life. The exercise group showed a 7% greater improvement than the control group in the Parkinson’s disease falls risk score, but this was not statistically significant (95% Confidence Interval (95% CI) -20 to 5) and the clinical relevance of this small reduction is uncertain. There were statistically significant improvements in the exercise group compared to the control group for two secondary outcomes which were not part of the falls risk score: Freezing of Gait Questionnaire (mean between-group difference = -2.8, 95% CI -5.4 to -0.3) and timed sit to stand (mean between-group difference = -1.9 s, 95% CI -3.6 to -0.2). There were non-significant trends toward greater improvements in the exercise group for other measures that were not part of the falls risk score, including muscle strength (stronger leg P = 0.06), fast walking speed (P = 0.21) and fear of falling (P = 0.10), but not for balance or quality of life. The exercise group had no major adverse events. Therefore, a minimally-supervised exercise program for mobile people with Parkinson’s disease who are at risk of falling might reduce overall risk of falling and improve muscle strength and can improve freezing and sit to stand speed. The results of this study have informed the implementation of a larger randomised controlled trial to assess whether this relatively small reduction in fall risk translates into actual falls prevented. Reduced balance is a commonly experienced risk factor for falls in people with Parkinson’s disease. However, the effect of exercise and motor training on balance in people with Parkinson’s disease was unclear and had not been subjected to meta-analysis. A systematic review with random effects meta-analysis was conducted to determine the effects of exercise and motor training on balance-related activity performance in people with Parkinson’s disease. Meta-regression was used to investigate if the total dose of exercise and the presence of highly-challenging balance training are associated with the size of the effect of intervention on balance-related activities. Seven electronic databases were searched in September 2009. Trials were included if they were published randomised controlled trials of an intervention designed for people with Parkinson’s disease that compared exercise and/or motor training with a no intervention or placebo control group, and were evaluated with a measure of balance. The primary outcome measures were balance-related activity performance and falls. The balance-related activity performance measure involved pooling the single most comprehensive balance measure from each trial and included (in order of priority): the Berg Balance Scale, the Timed Up and Go, gait velocity/time, turning time, sit to stand time, Functional Reach and single leg stand time. The outcomes were included in this order to prioritise outcomes the author considered to be the most global measures of balance or balance-related activity performance. Secondary outcome measures included these individual balance measures as discrete measures as well as step/stride length and cadence. The balance-related activity performance meta-analysis included 15 trials with 747 participants and the falls meta-analysis included 2 trials with 250 participants. The pooled estimate of the effect showed that exercise and motor training significantly improved balance-related activity performance (Hedges’ g = 0.34, 95% CI 0.11 to 0.57, P = 0.004) but there was no evidence of an effect on the proportion of fallers (risk ratio = 1.02; 95% CI 0.66 to 1.58, P = 0.94). Exercise and motor training was found to have a small positive effect on gait velocity and step/stride length as well as a moderate effect on turning time. The greatest relative effects of exercise and motor training on balance-related activity performance tended to occur in programs with highly-challenging balance training (P = 0.16), but there was no evidence of an association with the total dose of exercise (P = 0.98). There were non-significant trends towards improvement for most other outcome measures. Therefore, exercise and motor training can improve the performance of balance-related activities in people with Parkinson’s disease. However, further research is required to determine if falls can be prevented using exercise approaches in this population. While leg muscle weakness is a risk factor for falls in people with Parkinson’s disease, muscle strength is not commonly considered to be affected by the disease process and muscle weakness is usually not apparent on clinical examination. However, people with Parkinson’s disease often report feeling weak. One of the reasons for this discrepancy is the presence of bradykinesia (slowness of movement), making it difficult to ascertain if people with Parkinson’s disease are truly weak, or just slow to develop muscle force. The measurement of muscle power (force × velocity of contraction) has the potential to clarify the relationship between muscle weakness and bradykinesia in people with Parkinson’s disease. Furthermore, in the general older population, muscle power appears to be a better predictor of falls and physical activity performance than muscle strength. While modern variable resistance technology has the ability to measure muscle strength without the interference of bradykinesia, as well as muscle power, these measurements had never been reported in people with Parkinson’s disease. A descriptive study with two parts was conducted utilising this technology. Part one aimed to determine if the leg extensor muscles of people with mild to moderate Parkinson’s disease are weaker and/or less powerful than a neurologically-normal control group, and determine the relative contributions of force and movement velocity to muscle power in people with Parkinson’s disease. The leg extensor muscle strength (N) and power (W) of 40 participants with Parkinson’s disease and 40 neurologically-normal participants of similar age and gender were assessed. The Parkinson’s disease group were 16% weaker (mean between-group difference = 172N, 95% CI 28 to 315) and 22% less powerful (mean between-group difference = 124 W, 95% CI 32 to 216) than the control group. Muscle power was disproportionately reduced at light to medium loads due to reduced movement speed, whereas at heavy loads this bradykinesia was no longer apparent. These results suggest that reduced muscle power at lighter loads arises from weakness and bradykinesia combined, but at heavier loads arises primarily from weakness. Part two aimed to examine the relationship of muscle strength/power with walking speed and past falls in people with Parkinson’s disease. Walking velocity over 10 m and the number of falls experienced in the prior 12 months was recorded for the 40 aforementioned participants with Parkinson’s disease. Muscle power was found to explain more than half the variance in walking velocity (R2 = 0.54) and remained significantly associated with walking velocity in models which included a measure of Parkinson’s disease severity. Furthermore, participants with low muscle power were 6 times more likely to report multiple falls in the prior 12 months than those with high muscle power (Odds Ratio = 6.0, 95% CI 1.1 to 33.3), although this association between falls and power was no longer significant in models which included Parkinson’s disease severity. However, muscle power was consistently found to explain as much or more of the variation in walking velocity, and was more strongly associated with past falls, than muscle strength. Adequate leg extensor muscle power therefore seems likely to be important for maximising mobility and reducing fall risk in people with Parkinson’s disease. A Parkinson’s disease fall risk score that includes leg muscle power instead of strength should be trialled, and the effect of muscle power training on walking speed and falls in people with Parkinson’s disease warrants investigation. Overall, the research presented in this thesis provides three pieces of evidence related to exercise and fall risk in people with Parkinson’s disease. Firstly, exercise interventions can improve freezing of gait and are likely to improve muscle weakness and balance in people with Parkinson’s disease. Improvements in these potentially remediable risk factors for falls may lead to a reduction in the overall risk of falling in this group. Secondly, it appears that muscle weakness and reduced muscle power are due, in part, to the disease process itself. Finally, reduced muscle power is likely to also be a risk factor for falls in Parkinson’s disease and may be more important to address with exercise interventions than muscle weakness. These results provide evidence to assist clinicians and researchers in devising exercise programs for people with Parkinson’s disease. Any reduction in falls in this group will improve the quality of life of people with Parkinson’s disease and their carers and help to reduce pressure on health care systems

Cortical activity during walking and balance tasks in older adults and in people with Parkinson’s disease: a structured review

An emerging body of literature has examined cortical activity during walking and balance tasks in older adults and in people with Parkinson’s disease, specifically using functional near infrared spectroscopy (fNIRS) or electroencephalography (EEG). This review provides an overview of this developing area, and examines the disease-specific mechanisms underlying walking or balance deficits. Medline, PubMed, PsychInfo and Scopus databases were searched. Articles that described cortical activity during walking and balance tasks in older adults and in those with PD were screened by the reviewers. Thirty-seven full-text articles were included for review, following an initial yield of 566 studies. This review summarizes study findings, where increased cortical activity appears to be required for older adults and further for participants with PD to perform walking and balance tasks, but specific activation patterns vary with the demands of the particular task. Studies attributed cortical activation to compensatory mechanisms for underlying age- or PD-related deficits in automatic movement control. However, a lack of standardization within the reviewed studies was evident from the wide range of study protocols, instruments, regions of interest, outcomes and interpretation of outcomes that were reported. Unstandardized data collection, processing and reporting limited the clinical relevance and interpretation of study findings. Future work to standardize approaches to the measurement of cortical activity during walking and balance tasks in older adults and people with PD with fNIRS and EEG systems is needed, which will allow direct comparison of results and ensure robust data collection/reporting. Based on the reviewed articles we provide clinical and future research recommendations

A new machine learning based approach to predict Freezing of Gait

Freezing of gait (FoG) is a motor symptom of Parkinson’s disease (PD) that frequently occurs in the long-term sufferers of the disease. FoG may result to nursing home admission as it can lead to falls, and therefore, it impacts negatively on the quality of life. The focus of this study is the systematic evaluation of machine learning techniques in conjunction with varying size time windows and time/frequency domain feature sets in predicting a FoG event before its onset. In the experiments, the Daphnet FoG dataset is used to benchmark performance. This consists of accelerometer signals obtained from sensors mounted on the ankle, thigh and trunk of the PD patients. The dataset is annotated with instances of normal activity events, and FoG events. To predict the onset of FoG, the dataset is augmented with an additional class, termed ‘transition’, which relates to a manually defined period prior to the occurrence of a FoG episode. In this research, five machine learning models are used, namely, Random Forest, Extreme Gradient Boosting, Gradient Boosting, Support Vector Machines using Radial Basis Functions, and Neural Networks. Support Vector Machines with Radial Basis kernels provided the best performance achieving sensitivity values of 72.34%, 91.49%, 75.00%, and specificity values of 87.36%, 88.51% and 93.62%, for the FoG, transition and normal activity classes, respectivel

Falls in Parkinson's disease and Huntington's disease

Falls in Parkinson__s (PD) and Huntington__s disease (HD) are common. 50 % of moderately affected PD patients sustained two or more falls during a prospective follow-up of 6 months. During a 3 month period 40 % of HD patients reported one or more fall. Many falls resulted in minor injuries and 42 % of PD patients reported a fear of future falls. A different study on quality of life in PD showed that quality of life scores were significantly related to fear of falling in PD. In order to predict future falls several clinical tests and risk factors were studied. However, it proved difficult to identify future fallers and asking for prior falls was the best predictor of falls in the near future in PD. Analysis with quantative measurements in HD patients showed that an increased medio lateral trunk sway and a decreased stride length were associated with an increased fall risk. Based on the findings in these studies and on a literature study, the thesis concludes with a proposal for a multidisciplinary intervention program to prevent falls in Parkinson__s disease.UBL - phd migration 201