ABSTRACT
PURPOSE: Freezing of gait (FOG) is a disabling phenomenon defined by the periodic absence or reduction of forward progression of the feet despite the intention to walk. We sought to understand whether Google Glass (GG), a lightweight wearable device that provides simultaneous visual-auditory cues, might improve FOG in parkinsonism. METHODS: Patients with parkinsonism and FOG utilized GG custom-made auditory-visual cue applications: "Walk With Me" and "Unfreeze Me" in a single session intervention. We recorded ambulation time with and without GG under multiple conditions including 25 feet straight walk, dual task of performing serial 7's while straight walking, 180 degree turn after walking 25 feet, and walking through a doorway. FOG and patient experience questionnaires were administered. RESULTS: Using the GG "Walk With Me" program, improvements were noted in the following: average 25 feet straight walk by 0.32 s (SD 2.12); average dual task of serial 7's and 25 feet straight walk by 1.79 s (SD 2.91); and average walk through doorway by 0.59 s (SD 0.81). Average 180 degree turn after 25 feet walk worsened by 1.89 s (SD 10.66). Using the "Unfreeze Me" program, only the average dual task of serial 7's and 25 feet straight walk improved (better by 0.82 s (SD 3.08 sec). All other tasks had worse performance in terms of speed of completion. CONCLUSION: This feasibility study provides preliminary data suggesting that some walking tasks may improve with GG, which uses various musical dance programs to provide visual and auditory cueing for patients with FOG.IMPLICATIONS FOR REHABILITATIONFreezing of gait in parkinsonian syndromes is a disabling motor block described by patients as having their feet stuck to the floor leading to difficulty in initiation of gait and increased risk for falls.Wearable assistive devices such as Google Glass™ use visual and auditory cueing that may improve gait pattern in patients with freezing of gait.Augmented reality programs using wearable assistive devices are a home-based therapy, with the potential for reinforcing physical therapy techniques; this is especially meaningful during the COVID-19 pandemic when access to both medical and rehabilitative care has been curtailed.
ABSTRACT
Background: Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment. Objective: A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital. Methods: In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model. Results: We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%. Conclusion: A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.