Impact of inability to turn in bed assessed by a wearable three-axis accelerometer on patients with Parkinson's disease

A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods.

Assessing impaired bed mobility in patients with Parkinson's disease: a scoping review

BACKGROUND

Although most patients with Parkinson's disease (PD) experience difficulties in bed mobility, evidence on the suitability of the methods for assessing impaired bed mobility in PD are lacking.

OBJECTIVES

To identify objective methods for assessing impaired bed mobility in PD and to discuss their clinimetric properties and feasibility for use in clinical practice.

DATA SOURCES

PubMed, Web of Science, and Cochrane Library were searched between 1995 and 2022.

SELECTION CRITERIA

Studies were included if they described an objective assessment method for assessing impaired bed mobility in PD.

DATA EXTRACTION AND DATA SYNTHESIS

Characteristics of the identified measurement methods such as clinimetric properties and feasibility were extracted by two authors. The methodological quality of studies was evaluated using the Appraisal of studies tool.

RESULTS

Twenty-three studies were included and categorised into three assessment methods: sensor-based assessments (48%), rating scales (39%), and timed-tests (13%). The risk of bias was low for all but one study, which was medium.

LIMITATIONS

Despite applying wide selection criteria, a relatively small number of studies were identified in our results.

CONCLUSION

Rating scales may be the most preferred for assessing impaired bed mobility in PD in clinical practice, until clinimetric validity are adequately demonstrated in the other assessment methods. CONTRIBUTION OF PAPER.

Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?

BACKGROUND

Wearables (= wearable computer) enable continuous and noninvasive monitoring of a range of vital signs. Mobile and cost-effective devices, combined with powerful data analysis tools, open new dimensions in assessing body functions ("digital biomarkers").

METHODS

To answer the question whether wearables are ready for use in the medical context, a PubMed literature search and analysis for their clinical-scientific use using publications from the years 2008 to 2018 was performed.

RESULTS

A total of 79 out of 314 search hits were publications on clinical trials with wearables, of which 16 were randomized controlled trials. Motion sensors were most frequently used to measure defined movements, movement disorders, or general physical activity. Approximately 20% of the studies used sensors to detect cardiovascular parameters. As for the sensor location, the wrist was chosen in most studies (22.8%).

CONCLUSION

Wearables can be used in a precisely defined medical context, when taking into account complex influencing factors.

Sleep Issues in Parkinson's Disease and Their Management

Parkinson's disease (PD) is an alpha-synucleinopathy that leads to prominent motor symptoms including tremor, bradykinesia, and postural instability. Nonmotor symptoms including autonomic, neurocognitive, psychiatric symptoms, and sleep disturbances are also seen frequently in PD. The impact of PD on sleep is related to motor and nonmotor symptoms, in addition to the disruption of the pathways regulating sleep by central nervous system pathology. Rapid eye movement sleep behavior disorder is a parasomnia that can lead to self-injury and/or injury to partners at night. Restless legs syndrome is a subjective sensation of discomfort and urge to move the legs prior to falling asleep and can lead to insomnia and reduced sleep quality. Excessive daytime sleepiness is common in PD and exerts a negative impact on quality of life in addition to increasing the risk of falls. Obstructive sleep apnea is a breathing disorder during sleep that can cause frequent awakenings and excessive daytime sleepiness. Circadian rhythm dysfunction can lead to an advanced or delayed onset of sleep in patients and create disruption of normal sleep and wake times. All of these disorders are common in PD and can significantly reduce sleep quantity, sleep quality, or quality of life for patients and caretakers. Treatment approaches for each of these disorders are distinct and should be individualized to the patient. We review the literature regarding these common sleep issues encountered in PD and their treatment options.

Shedding Light on Nocturnal Movements in Parkinson's Disease: Evidence from Wearable Technologies

In Parkinson’s disease (PD), abnormal movements consisting of hypokinetic and hyperkinetic manifestations commonly lead to nocturnal distress and sleep impairment, which significantly impact quality of life. In PD patients, these nocturnal disturbances can reflect disease-related complications (e.g., nocturnal akinesia), primary sleep disorders (e.g., rapid eye movement behaviour disorder), or both, thus requiring different therapeutic approaches. Wearable technologies based on actigraphy and innovative sensors have been proposed as feasible solutions to identify and monitor the various types of abnormal nocturnal movements in PD. This narrative review addresses the topic of abnormal nocturnal movements in PD and discusses how wearable technologies could help identify and assess these disturbances. We first examine the pathophysiology of abnormal nocturnal movements and the main clinical and instrumental tools for the evaluation of these disturbances in PD. We then report and discuss findings from previous studies assessing nocturnal movements in PD using actigraphy and innovative wearable sensors. Finally, we discuss clinical and technical prospects supporting the use of wearable technologies for the evaluation of nocturnal movements.

Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment

BACKGROUND

The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities.

OBJECTIVE

This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used.

METHODS

Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants.

RESULTS

65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living.

CONCLUSIONS

Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.

Deep Phenotyping of Parkinson's Disease

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.