Graph Fusion Network-Based Multimodal Learning for Freezing of Gait Detection

Reliability and variability of physiotherapists scoring freezing of gait through video analysis

BACKGROUND

The "gold standard" marker for freezing of gait severity is percentage of time spent with freezing observed through video analysis.

OBJECTIVE

This study examined inter- and intra-rater reliability and variability of physiotherapists rating freezing of gait severity through video analysis and explored the effects of experience.

METHODS

Thirty physiotherapists rated 14 videos of Timed Up and Go performance by people with Parkinson's and gait freezing. Ten videos were unique, while four were repeated. Freezing frequency, total duration, and percentage of time spent with freezing were computed. Reliability and variability were estimated using ICC (2,1) and mean absolute differences. Between-group differences were calculated with the one-way ANOVA.

RESULTS

Inter- and intra-rater reliability ranged from moderate to good (ICC: inter-rater frequency = 0.63, duration = 0.78, percentage = 0.50; intra-rater frequency = 0.84, duration = 0.89, percentage = 0.50). Variability for freezing frequency was two episodes. Inter- and intra-rater variability for total freezing duration was 18.8 and 12.3  seconds, respectively. For percentage of time spent with freezing, this was 15.2% and 13.5%. Physiotherapy experience had no effect.

CONCLUSION

Physiotherapists demonstrated sufficient reliability, but variability was large enough to cause changes in severity classifications on existing rating scales. Percentage of time spent with freezing was the least reliable marker, supporting the use of freezing frequency or total duration instead.

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

Acceptability of an In-Home Multimodal Sensor Platform in Parkinson’s Disease: A Qualitative Study (Preprint)

Background

Parkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD.

Objective

The primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors.

Methods

A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed.

Results

This study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants.

Conclusions

The results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2020-041303