Double-Tap Interaction as an Actuation Mechanism for On-Demand Cueing in Parkinson's Disease.

Freezing of Gait (FoG) is one of the most debilitating symptoms of Parkinson's disease and is an important contributor to falls. When the management of freezing episodes cannot be achieved through medication or surgery, non-pharmacological methods, such as cueing, have emerged as effective techniques, which ameliorates FoG. The use of On-Demand cueing systems (systems that only provide cueing stimuli during a FoG episode) has received attention in recent years. For such systems, the most common method of triggering the onset of cueing stimuli, utilize autonomous real-time FoG detection algorithms. In this article, we assessed the potential of a simple double-tap gesture interaction to trigger the onset of cueing stimuli. The intended purpose of our study was to validate the use of double-tap gesture interaction to facilitate Self-activated On-Demand cueing. We present analyses that assess if PwP can perform a double-tap gesture, if the gesture can be detected using an accelerometer's embedded gestural interaction recognition function and if the action of performing the gesture aggravates FoG episodes. Our results demonstrate that a double-tap gesture may provide an effective actuation method for triggering On-Demand cueing. This opens up the potential future development of self-activated cueing devices as a method of On-Demand cueing for PwP and others.

A Technological Review of Wearable Cueing Devices Addressing Freezing of Gait in Parkinson's Disease.

Freezing of gait is one of the most debilitating symptoms of Parkinson's disease and is an important contributor to falls, leading to it being a major cause of hospitalization and nursing home admissions. When the management of freezing episodes cannot be achieved through medication or surgery, non-pharmacological methods such as cueing have received attention in recent years. Novel cueing systems were developed over the last decade and have been evaluated predominantly in laboratory settings. However, to provide benefit to people with Parkinson's and improve their quality of life, these systems must have the potential to be used at home as a self-administer intervention. This paper aims to provide a technological review of the literature related to wearable cueing systems and it focuses on current auditory, visual and somatosensory cueing systems, which may provide a suitable intervention for use in home-based environments. The paper describes the technical operation and effectiveness of the different cueing systems in overcoming freezing of gait. The "What Works Clearinghouse (WWC)" tool was used to assess the quality of each study described. The paper findings should prove instructive for further researchers looking to enhance the effectiveness of future cueing systems.

Techno-economic Analysis and Optimization of Intensified, Large-Scale Hydrogen Production with Membrane Reactors.

Steam methane reforming (SMR) currently supplies 76% of the world's hydrogen (H2) demand, totaling ∼70 million tonnes per year. Developments in H2 production technologies are required to meet the rising demand for cleaner, less costly H2. Therefore, palladium membrane reactors (Pd-MR) have received significant attention for their ability to increase the efficiency of traditional SMR. This study performs novel economic analyses and constrained, nonlinear optimizations on an intensified SMR process with a Pd-MR. The optimization extends beyond the membrane's operation to present process set points for both the conventional and intensified H2 processes. Despite increased compressor and membrane capital costs along with electric utility costs, the SMR-MR design offers reductions in the natural gas usage and annual costs. Economic comparisons between each plant show Pd membrane costs greater than $25 000/m2 are required to break even with the conventional design for membrane lifetimes of 1-3 years. Based on the optimized SMR-MR process, this study concludes with sensitivity analyses on the design, operational, and cost parameters for the intensified SMR-MR process. Overall, with further developments of Pd membranes for increased stability and lifetime, the proposed SMR-MR design is thus profitable and suitable for intensification of H2 production.

Applicability and tolerability of electrical stimulation applied to the upper and lower leg skin surface for cueing applications in Parkinson's disease.

Due to possible sensory impairments in people with Parkinson's disease, several methodological aspects of electrical stimulation as a potential cueing method remain to be explored. This study aimed to investigate the applicability and tolerability of sensory and motor electrical stimulation in 10 people with Parkinson's disease. The study focused on assessing the electrical stimulation voltages and visual analogue scale discomfort scores at the electrical sensory, motor, discomfort, and pain thresholds. Results show that sensory electrical stimulation at the tibialis anterior, soleus, hamstrings, and quadriceps stimulation sites was applicable and tolerable for 6/10, 10/10, 9/10, and 10/10 participants, respectively. Furthermore, motor electrical stimulation at the tibialis anterior, soleus, hamstrings, and quadriceps stimulation sites were applicable and tolerable for 7/10, 7/10, 7/10, and 8/10 participants, respectively. Interestingly, the thresholds for the lower leg were higher than those of the upper leg. The data presented in this paper indicate that sensory and motor electrical stimulation is applicable and tolerable for cueing applications in people with Parkinson's disease. Sensory electrical stimulation was applicable and tolerable at the soleus and quadriceps sites. Motor electrical stimulation was not tolerable for two participants at any of the proposed stimulation sites. Therefore, future studies investigating motor electrical stimulation cueing, should apply it with caution in people with Parkinson's disease.

Estimating dyskinesia severity in Parkinson's disease by using a waist-worn sensor: concurrent validity study.

Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson's disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson's patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient's activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician's assessment and the sensor output was analyzed with the Spearman's correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33-0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76-0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.

Sensory Electrical Stimulation Cueing May Reduce Freezing of Gait Episodes in Parkinson's Disease.

Introduction: Freezing of gait (FoG) is a movement abnormality that presents with advancing Parkinson's disease (PD) and is one of the most debilitating symptoms of the disease. The mainstay of nonpharmacological management of FoG is typically through external cueing techniques designed to relieve or prevent the freezing episode. Previous work shows that electrical stimulation may prove useful as a gait guidance technique, but further evidence is required. The main objective of this study was to determine whether a "fixed" rhythmic sensory electrical stimulation (sES) cueing strategy would significantly (i) reduce the time taken to complete a walking task and (ii) reduce the number of FoG episodes occurring when performing the task. Methods: 9 participants with idiopathic PD performed a self-identified walking task during both control (no cue) and cueing conditions. The self-identified walking task was a home-based daily walking activity, which was known to result in FoG for that person. A trained physiotherapist recorded the time taken to complete the walking task and the number of FoG episodes which occurred during the task. Data were analyzed by paired t-tests for both the time to complete a walking task and the number of FoG episodes occurring. Results: sES cueing resulted in a reduction in the time taken to complete a walking task and in the number of FoG episodes occurring during performance of this task by 14.23 ± 11.15% (p=0.009) and 58.28 ± 33.89% (p=0.002), respectively. Conclusions: This study shows a positive effect of "fixed" rhythmic sES on the time taken to complete a walking task and on the number of FoG episodes occurring during the task. Our results provide evidence that sES cueing delivered in a "fixed" rhythmic manner has the potential to be an effective cueing mechanism for FoG prevention.

A "HOLTER" for Parkinson's disease: Validation of the ability to detect on-off states using the REMPARK system.

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest. OBJECTIVE: To analyze the ability of the REMPARK System to detect ON-OFF fluctuations. METHODS: Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson's Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3days and completed a diary of their motor state once every hour. RESULTS: The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states). CONCLUSION: The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

BACKGROUND: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III). METHOD: Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient. RESULTS: Correlation with the UPDRS-III was moderate (rho -0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 (p < 0.01). CONCLUSION: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.

Corrigendum: Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

[This corrects the article on p. 431 in vol. 8, PMID: 28919877.].

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.

Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients.

Freezing of gait (FOG) is a common motor symptom of Parkinson's disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier's outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7% accuracy and a geometric mean of 96.1%. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90% and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer.

BACKGROUND: After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. OBJECTIVE: To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS: Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS: Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION: The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

Smartphone app design for the wireless control of a neuromuscular electrical stimulator device with integrated randomization allocation process for RCT applications.

The use of NMES has evolved over the last five decades. Technological advancements have transformed these once complex systems into user-friendly devices with enhanced control functions, leading to new applications of NMES being investigated. The use of Randomized Control Trial (RCT) methodology in evaluating the effectiveness of new and existing applications of NMES is a demanding process adding time and cost to a translation into clinical practice. Poor quality trials may result in poor evidence of NMES effectiveness. In this paper some of the key challenges encountered in NMES clinical trials are identified with the aim of purposing a solution to address these challenges through the adoption of Smartphone technology. The design and evaluation of a smartphone application to provide automatic blind randomization control and facilitating the wireless temporal control of a portable Bluetooth enabled NMES is presented.