The transition between turning and sitting in patients with Parkinson's disease: A wearable device detects an unexpected sequence of events

Can Dance Improve Turning in People With Parkinson's Disease?

Objective: To investigate the effects of a dance intervention on selected functional parameters during the 180° turning phase of the Timed Up & Go (TUG) test in people with Parkinson's Disease (PwPD). Methods: Fifteen adults clinically diagnosed with idiopathic PD were allocated into dance intervention (DIG; n = 7 ; age 73 ± 2 years) and control (CG; n = 8; age 64 ± 5 years) groups. The dance intervention lasted for 3 months (1 hour, twice a week). At baseline, all participants completed the Unified PD Rating Scale-part III, the International Physical Activity Questionnaire-short form, and the Hoehn & Yahr scale. Pre- and post-intervention, the primary outcomes were measured (number of steps and time to complete the 180° turning phase of the TUG test) at 2 speeds (comfortable walking and as quickly and safely speed) while using the Xsens® 3D motion suit. The secondary outcome (girdle dissociation) was assessed by calculating the difference between pelvis and affected shoulder orientation in the transverse plane (dissociation angles) at each data point during the TUG test's 180° turning phase. Results: At participant's comfortable walking speed, the functionality during the 180° turning remained unaffected following the dance intervention. However, at participant's fast speed, the dance intervention group significantly reduced the number of steps with a large effect size, and the total time taken to complete the 180° turning with a medium effect size. Post-intervention, most participants in the dance intervention group reduced the affected shoulder and pelvic girdle dissociation and turned more "en bloc." Conclusion: Dance can improve selected functional parameters during the 180° turning at fast speed in PwPD. The current results should be considered in rehabilitation programs.

Digital biomarkers in Parkinson's disease

Digital biomarker (DB) assessments provide objective measures of daily life tasks and thus hold promise to improve diagnosis and monitoring of Parkinson's disease (PD) patients especially those with advanced stages. Data from DB studies can be used in advanced analytics such as Artificial Intelligence and Machine Learning to improve monitoring, treatment and outcomes. Although early development of inertial sensors as accelerometers and gyroscopes in smartphones provided encouraging results, the use of DB remains limited due to lack of standards, harmonization and consensus for analytical as well as clinical validation. Accordingly, a number of clinical trials have been developed to evaluate the performance of DB vs traditional assessment tools with the goal of monitoring disease progression, improving quality of life and outcomes. Herein, we update current evidence on the use of DB in PD and highlight potential benefits and limitations and provide suggestions for future research study.

Long-term effects of bilateral subthalamic nucleus deep brain stimulation on gait disorders in Parkinson's disease: a clinical-instrumental study

OBJECTIVE

To assess the long-term effects of bilateral subthalamic nucleus deep brain stimulation (STN-DBS) on gait in a cohort of advanced Parkinson's Disease (PD) patients.

METHODS

This observational study included consecutive PD patients treated with bilateral STN-DBS. Different stimulation and drug treatment conditions were assessed: on-stimulation/off-medication, off-stimulation/off-medication, and on-stimulation/on-medication. Each patient performed the instrumented Timed Up and Go test (iTUG). The instrumental evaluation of walking ability was carried out with a wearable inertial sensor containing a three-dimensional (3D) accelerometer, gyroscope, and magnetometer. This device could provide 3D linear acceleration, angular velocity, and magnetic field vector. Disease motor severity was evaluated with the total score and subscores of the Unified Parkinson Disease Rating Scale part III.

RESULTS

Twenty-five PD patients with a 5-years median follow-up after surgery (range 3-7) were included (18 men; mean disease duration at surgery 10.44 ± 4.62 years; mean age at surgery 58.40 ± 5.73 years). Both stimulation and medication reduced the total duration of the iTUG and most of its different phases, suggesting a long-term beneficial effect on gait after surgery. However, comparing the two treatments, dopaminergic therapy had a more marked effect in all test phases. STN-DBS alone reduced total iTUG duration, sit-to-stand, and second turn phases duration, while it had a lower effect on stand-to-sit, first turn, forward walking, and walking backward phases duration.

CONCLUSIONS

This study highlighted that in the long-term after surgery, STN-DBS may contribute to gait and postural control improvement when used together with dopamine replacement therapy, which still shows a substantial beneficial effect.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Turning and Sitting in Early Parkinsonism: Differences Between Idiopathic Normal Pressure Hydrocephalus Associated with Parkinsonism and Parkinson's Disease

Background

Differential diagnosis between idiopathic normal pressure hydrocephalus (iNPH) associated with parkinsonism (iNPH-P) and Parkinson's disease (PD) may prove difficult when evaluating patients with early parkinsonism. The objective of this study was to evaluate differences in mobility during standardized tasks between iNPH-P and PD.

Methods

We selected 21 iNPH-P and 21 pharmacologically untreated PD patients. They all performed the instrumented Timed Up and Go test at the time of diagnosis.

Results

Turning tasks showed longer duration and lower speed in iNPH-P than in PD. Vertical variation in acceleration during the sit-to-stand phase was lower in iNPH-P patients, whereas the duration of the stand-to-sit phase was longer. On walking, iNPH-P showed smaller stride length and a longer gait cycle duration. In multivariate analysis adjusting for age and cognitive status as potential confounders, average angular speed on turning before sitting was the discriminating parameter between the two groups.

Conclusions

Patients with iNPH-P showed specific abnormal mobility performances with respect to untreated PD, specifically during the turning-to-sitting transition.

Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

Classification of Parkinson's disease with freezing of gait based on 360° turning analysis using 36 kinematic features

BACKGROUND

Freezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.

METHODS

The study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.

RESULTS

In feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.

CONCLUSION

We suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

Digital health technology for non-motor symptoms in people with Parkinson's disease: Futile or future?

INTRODUCTION

There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS).

METHODS

A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar.

RESULTS

Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD.

CONCLUSION

Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning

BACKGROUND

It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD).

OBJECTIVE

To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage.

METHODS

Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages.

RESULTS

High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages.

CONCLUSIONS

Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

The effect of vibratory stimulation on the timed-up-and-go mobility test: a pilot study for sensory-related fall risk assessment

Effects of localized lower-extremity vibration on postural balance have been reported. The purpose of the current study was to investigate the effect of low-frequency vibration of calf muscles on the instrumented Timed-Up-and-Go (iTUG) test among older adults. Older adults were recruited and classified to low (n=10, age=72.9±2.8 years) and high fall risk (n=10, age=83.6±9.6) using STEADI. Vibratory system (30Hz or 40Hz), was positioned on calves along with wearable motion sensors. Participants performed the iTUG test three times, under conditions of no-vibration, 30Hz, and 40Hz vibration. Percentage differences in duration of iTUG components were calculated comparing vibration vs no-vibration conditions. Significant between-group differences were observed in iTUG (p=0.03); high fall risk participants showed reduction in the duration of turning (-10 % with 30Hz; p=0.15 and -15 % with 40Hz; p=0.03) and turning and sitting (-18 % with 30Hz; p=0.02 and -10 % with 40Hz; p=0.08). However, vibration increased turning (+18 % with 30Hz; p=0.20 and +27 % with 40Hz; p=0.12) and turning and sitting duration (+27 % with 30Hz; p=0.11 and +47 % with 40Hz; p=0.12) in low fall risk participants. Findings suggest that lower-extremity vibration affects dynamic balance; however, the level of this influence may differ between low and high fall risk older adults, which can potentially be used for assessing aging-related sensory deficits.

Assessment of Functional Activities in Individuals with Parkinson's Disease Using a Simple and Reliable Smartphone-Based Procedure

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to functional impairment. In order to monitor the progression of the disease and to implement individualized therapeutic approaches, functional assessments are paramount. The aim of this study was to determine the impact of PD on balance, gait, turn-to-sit and sit-to-stand by means of a single short-duration reliable test using a single inertial measurement unit embedded in a smartphone device. Study participants included 29 individuals with mild-to moderate PD (PG) and 31 age-matched healthy counterparts (CG). Functional assessment with FallSkip^® included postural control (i.e., Medial-Lateral (ML) and Anterior-Posterior (AP) displacements), gait (Vertical (V) and Medial-Lateral (ML) ranges), turn-to-sit (time) and sit-to-stand (power) tests, total time and gait reaction time. Our results disclosed a reliable procedure (intra-class correlation coefficient (ICC) = 0.58–0.92). PG displayed significantly larger ML and AP displacements during the postural test, a decrease in ML range while walking and a longer time needed to perform the turn-to-sit task than CG (p < 0.05). No differences between groups were found for V range, sit-to-stand test, total time and reaction time (p > 0.05). In conclusion, people with mild-to-moderate PD exhibit impaired postural control, altered gait strategy and slower turn-to-sit performance than age-matched healthy people.

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.

Transitional Locomotor Tasks in People With Mild to Moderate Parkinson's Disease

Background: People with Parkinson's disease (PD) exhibit deficits in maintaining balance both during quiet standing and during walking, turning, standing up from sitting, and step initiation. Objective: The purpose of this study was to examine balance disorders during a transitional task under different conditions in participants with PD. Methods: The research was conducted on 15 PD-II (mild) and 15 PD-III (moderate) individuals (H&Y II-III stage) and 30 healthy elderly. The transitional task was measured on two force platforms (A and B). The procedure consisted of three phases: (1) quiet standing on platform A, (2) crossing to platform B, and (3) quiet standing on platform B, each until measurements were completed. There were four conditions: crossing without an obstacle, crossing with an obstacle, and walking up and down the step. Results: There were no significant differences between mild PD individuals and healthy elderly during quiet standing before the transitional task and after completing the task. The temporal aspects describing the different transitional tasks were comparable between mild PD and healthy subjects. Moderate PD participants presented a significantly higher COP velocity after the transitional task compared to the healthy older adults (p < 0.05). Additionally, the moderate PD group showed significantly higher values for transit time relative to healthy subjects during the transitional task in all conditions (p < 0.05). Conclusions: Disease severity affects the temporal aspects of different transitional tasks in people with PD. The procedure of completing a transitional task under different conditions allowed differences between moderate and mild PD stages and healthy subjects to be observed.

Implicit task switching in Parkinson's disease is preserved when on medication

People with Parkinson's disease have been shown to have difficulty switching between movement plans. In the great majority of studies, the need to switch between tasks was made explicitly. Here, we tested whether people with Parkinson's disease, taking their normal medication, have difficulty switching between implicitly specified tasks. We further examined whether this switch is performed predictively or reactively. Twenty five people with Parkinson's disease continuously increased or decreased the frequency of their arm movements, inducing an abrupt-but unaware-switch between rhythmic movements (at high frequencies) and discrete movements (at low frequencies). We tested whether that precipitous change was performed reactively or predictively. We found that 56% of participants predictively switched between the two movement types. The ability of people with Parkinson's disease, taking their regular medication, to predictively control their movements on implicit tasks is thus preserved.

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.

Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait - an analysis using inertial measurement units

Background

Impairments of functional mobility may affect locomotion and quality of life in subjects with Parkinson’s disease (PD). Movement smoothness measurements, such as the spectral arc length (SPARC), are novel approaches to quantify movement quality. Previous studies analyzed SPARC in simple walking conditions. However, SPARC outcomes during functional mobility tasks in subjects with PD and freezing of gait (FOG) were never investigated. This study aimed to analyze SPARC during the Timed Up and Go (TUG) test in individuals with PD and FOG.

Methods

Thirty-one participants with PD and FOG and six healthy controls were included. SPARC during TUG test was calculated for linear and angular accelerations using an inertial measurement unit system. SPARC data were correlated with clinical parameters: motor section of the Unified Parkinson’s Disease Rating Scale, Hoehn & Yahr scale, Freezing of Gait Questionnaire, and TUG test.

Results

We reported lower SPARC values (reduced smoothness) during the entire TUG test, turn and stand to sit in subjects with PD and FOG, compared to healthy controls. Unlike healthy controls, individuals with PD and FOG displayed a broad spectral range that encompassed several dominant frequencies. SPARC metrics also correlated with all the above-mentioned clinical parameters.

Conclusion

SPARC values provide valid and relevant clinical data about movement quality (e.g., smoothness) of subjects with PD and FOG during a functional mobility test.

Electronic supplementary material

The online version of this article (10.1186/s12984-019-0579-8) contains supplementary material, which is available to authorized users.

Review on Wearables to Monitor Foot Temperature in Diabetic Patients

One of the diseases that could affect diabetic patients is the diabetic foot problem. Unnoticed minor injuries and subsequent infection can lead to ischemic ulceration, and may end in a foot amputation. Preliminary studies have shown that there is a positive relationship between increased skin temperature and the pre⁻ulceration phase. Hence, we have carried out a review on wearables, medical devices, and sensors used specifically for collecting vital data. In particular, we are interested in the measure of the foot⁻temperature. Since there is a large amount of this type of medical wearables, we will focus on those used to measure temperature and developed in Spain.