Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

Ahlrichs, Claas; Samà, Albert; Lawo, Michael; Cabestany, Joan; Rodríguez-Martín, Daniel; Pérez-López, Carlos; Sweeney, Dean; Quinlan, Leo R.; Laighin, Gearóid Ó; Counihan, Timothy J.; Browne, Patrick; Lewy, Hadas; Vainstein, Gabriel; Costa, Alberto; Annicchiarico, Roberta; Alcaine, Sheila; Mestre, Berta; Quispe, Paola; Bayés, Àngels; Rodríguez‐Molinero, Alejandro

doi:10.1007/s11517-015-1395-3

Cited by 98 publications

(66 citation statements)

References 32 publications

(56 reference statements)

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“…However, improvements of micro characteristics due to pharmacological intervention (e.g. levodopa) can also be observed [98] However, advances of FOG detection within habitual environments mean wearable algorithms should not go unutilised [104,105] and be integrated to the type of analytical frameworks that could aid care [106,107].…”

Section: Pathologymentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) are one approach to offer a viable means for ubiquitous, sustainable and scalable monitoring in habitual free-living environments. Gait has been presented as a relevant (bio) marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of recent wearable gait assessment literature, focusing on the need for an algorithm fusion approach to measurement cumulating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Opportunities for how this domain needs to progress are also summarised. Highlights• Wearables can meet older adults' needs for independent living.• Gait assessment is a (bio)marker within ageing and different pathologies.• Measuring gait with wearables has been innovative but fraught with inconsistencies.

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Section: Pathologymentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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“…They have used ANN to predict the gait patters with approximately 95% accuracy. The Ahlrichs et al [30] used one tri-accelerometer device worn on the waist of the testing subjects used in detecting the freezing of gait (FOG) symptoms on the people suffering from Parkinson’s disease. The acceleration signals from a waist-mounted sensor are split into equally-sized windows (i.e., a sliding window is applied to the time series), and features are extracted from those windows and fed to an SVM for training or classification.…”

Section: Resultsmentioning

confidence: 99%

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

et al. 2016

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Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.

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“…Earlier research attempted to detect this anomalous movement by inertial sensors attached on the lower limbs (i.e., shank, thigh, and foot) [88]. In recent years, research has focused on other areas for easier mounting of the sensor, such as waist [89, 90] and wrist [91]. In this regard, a recent survey on user preferences for placement of such devices on the body found that the wrist, arm, and waist are much preferred than lower limbs among the patients (in this case, patients with osteoarthritis) [92].…”

Section: Clinical Applicationsmentioning

confidence: 99%

Quantitative Analysis of Motor Status in Parkinson’s Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

Suzuki

Mitoma

Yoneyama³

2017

Parkinson's Disease

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Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson's disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

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Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

Cited by 98 publications

References 32 publications

Wearables for independent living in older adults: Gait and falls

Wearables for independent living in older adults: Gait and falls

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Quantitative Analysis of Motor Status in Parkinson’s Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

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