Assessing inertial measurement unit locations for freezing of gait detection and patient preference

O’Day, Johanna J.; Lee, M. R.; Seagers, Kirsten; Hoffman, Shannon L.; Jih-Schiff, A. E.; Kidziński, Łukasz; Delp, Scott L.; Brontë‐Stewart, Helen

doi:10.1101/2021.09.27.21264041

Cited by 3 publications

(2 citation statements)

References 43 publications

(46 reference statements)

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“…Typically, inertial measurement units (IMUs) are placed on various limbs of subjects, including the feet, shanks, knees, etc., and their built-in accelerometers, gyroscopes, and magnetometers can record different features of PD movement. A recent study using IMUs coupled with convolutional neural network modeling to track FOG in PD subjects explored sensor locations that maximized patient adherence for personalized athome motor symptom monitoring (O Day et al, 2022). Additionally, smartphones can measure spatiotemporal features of rest tremor using a three-dimensional built-in accelerometer and robust software packages.…”

Section: Emerging Technologies In Assessing Motor Functionmentioning

confidence: 99%

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson’s disease: a narrative review

Kehnemouyi,

Coleman,

Tass

2024

Front. Netw. Physiol.

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Parkinson’s disease (PD) is a chronic movement disorder characterized by a variety of motor and nonmotor comorbidities, including cognitive impairment, gastrointestinal (GI) dysfunction, and autonomic/sleep disturbances. Symptoms typically fluctuate with different settings and environmental factors and thus need to be consistently monitored. Current methods, however, rely on infrequent rating scales performed in clinic. The advent of wearable technologies presents a new avenue to track objective measures of PD comorbidities longitudinally and more frequently. This narrative review discusses and proposes emerging wearable technologies that can monitor manifestations of motor, cognitive, GI, and autonomic/sleep comorbidities throughout the daily lives of PD individuals. This can provide more wholistic insight into real-time physiological versus pathological function with the potential to better assess treatments during clinical trials and allow physicians to optimize treatment regimens. Additionally, this narrative review briefly examines novel applications of wearables as therapy for PD patients.

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Section: Emerging Technologies In Assessing Motor Functionmentioning

confidence: 99%

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson’s disease: a narrative review

Kehnemouyi,

Coleman,

Tass

2024

Front. Netw. Physiol.

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“…In past studies, IMUs have shown to be both accurate and repeatable for measuring gait parameters in healthy young adults (Washabaugh et al, 2017). Additionally, publicly available datasets collected from patients with movement disorders using IMUs have been utilized to analyze gait parameters and freezing of gait episodes (O'Day et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients

Nouriani

Jonason

Sabal

et al. 2023

Front. Aging Neurosci.

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The use of wearable sensors in movement disorder patients such as Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over 1 year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.

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Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

Filtjens

Ginis

Nieuwboer

et al. 2022

J NeuroEngineering Rehabil

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Background Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson’s disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network. Methods Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. Results The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations. Conclusions The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.

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Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Cited by 3 publications

References 43 publications

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson’s disease: a narrative review

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson’s disease: a narrative review

Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

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