eWEAR: Freezing of Gait: Assessment of gait locations using wearables for Parkinson’s disease patients
Meeting Reports
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Nov 17, 2022
“Parkinson’s is my toughest fight. It doesn’t hurt. It’s hard to explain”, said former boxer Muhammed Ali who suffered from Parkinson’s disease. The increasing tremors in limbs, the painful slowness of gait, balance problems, and whispers of falls were indicative of the fact that Parkinsonism began its relentless march through Ali’s nervous system. The inability to move the feet forward despite the intention of walking is an enigmatic clinical phenomenon, a common symptom of PD, called Freezing of Gait (FOG). FOG is characterized by episodes of intermittent inability to step that occur on initiating gait or on turning while walking. FOG affects at least 50% of people suffering from PD.
Among the several ways of assessing FOG, objective and detailed analyses of gait are usually performed in specialized clinics by experienced raters that observe and video record patients’ walking around the clinic. Other subjective tools include the FOG questionnaire, patient surveys, and posture assessments by neurologists in a clinical setting. The visits to neurology clinics require patients to travel to purpose-built facilities and be tested in a clinical environment. This could affect gait patterns and could lead to infrequent assessment, resulting in less accurate tracking of disease progression and less effective tuning of therapy. Therefore, it is necessary to have a gait monitoring device that can enable FOG characterization in the patient’s daily surroundings and during normal activity. Inertial measurement units (IMU) are wearable devices that serve the purpose of successfully detecting and tracking movement enabling insights into FOG in any environment. Though several studies have used IMUs to detect FOG, they posed several limitations. Firstly, there is no consensus on where the IMUs are to be placed, nor did they account for the patient’s preference regarding the best body locations to place IMUs. The studies often relied on hand-engineered features, requiring extra labor, and potentially removing valuable information from the data collected.
To address these challenges, researchers from Stanford University, led by Prof. Helen BronteStewart and Prof. Scott Delp, assessed IMUs that people with PD can reliably wear based on FOG detection performance and patient preferences and discussed their findings in a recent report. A FOG detection algorithm was also created from a combination of patient surveys, IMU measurement data, and machine learning techniques. The dataset and framework developed by this study were intended to aid future research protocol development for FOG detection and monitoring and fine-tuning the personalization of the patient’s care.
“Parkinson’s is my toughest fight. It doesn’t hurt. It’s hard to explain”, said former boxer Muhammed Ali who suffered from Parkinson’s disease. The increasing tremors in limbs, the painful slowness of gait, balance problems, and whispers of falls were indicative of the fact that Parkinsonism began its relentless march through Ali’s nervous system. The inability to move the feet forward despite the intention of walking is an enigmatic clinical phenomenon, a common symptom of PD, called Freezing of Gait (FOG). FOG is characterized by episodes of intermittent inability to step that occur on initiating gait or on turning while walking. FOG affects at least 50% of people suffering from PD.
Among the several ways of assessing FOG, objective and detailed analyses of gait are usually performed in specialized clinics by experienced raters that observe and video record patients’ walking around the clinic. Other subjective tools include the FOG questionnaire, patient surveys, and posture assessments by neurologists in a clinical setting. The visits to neurology clinics require patients to travel to purpose-built facilities and be tested in a clinical environment. This could affect gait patterns and could lead to infrequent assessment, resulting in less accurate tracking of disease progression and less effective tuning of therapy. Therefore, it is necessary to have a gait monitoring device that can enable FOG characterization in the patient’s daily surroundings and during normal activity. Inertial measurement units (IMU) are wearable devices that serve the purpose of successfully detecting and tracking movement enabling insights into FOG in any environment. Though several studies have used IMUs to detect FOG, they posed several limitations. Firstly, there is no consensus on where the IMUs are to be placed, nor did they account for the patient’s preference regarding the best body locations to place IMUs. The studies often relied on hand-engineered features, requiring extra labor, and potentially removing valuable information from the data collected.
To address these challenges, researchers from Stanford University, led by Prof. Helen BronteStewart and Prof. Scott Delp, assessed IMUs that people with PD can reliably wear based on FOG detection performance and patient preferences and discussed their findings in a recent report. A FOG detection algorithm was also created from a combination of patient surveys, IMU measurement data, and machine learning techniques. The dataset and framework developed by this study were intended to aid future research protocol development for FOG detection and monitoring and fine-tuning the personalization of the patient’s care.
