Deep Learning for Detecting Freezing of Gait Episodes in Parkinson’s Disease Based on Accelerometers

Camps, Julià; Samà, Albert; Martín, Mario; Rodríguez-Martín, Daniel; Pérez-López, Carlos; Alcaine, Sheila; Mestre, Berta; Prats, Anna; Crespo, M. Cruz; Cabestany, Joan; Bayés, Àngels; Català, Andreu

doi:10.1007/978-3-319-59147-6_30

Cited by 25 publications

(19 citation statements)

References 16 publications

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“…Inertial measurement units have been used to quantify and classify gait temporal-spatial parameters [29]. Deep learning has been successfully used in the detection of freezing gait in people with Parkinson's disease [6], and deep neural networks have been used to differentiate between individuals based on gait pattern using both video footage [23] and data from body mounted sensors [10].…”

Section: A Related Workmentioning

confidence: 99%

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning

Turner

Hayes

2019

IEEE Trans. Biomed. Eng.

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This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom by symptom basis, irrespective of other neuromuscular movement disorders patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders. Methods: Inshoe pressure was measured for 12 able-bodied participants, each subject to 8 artificially induced gait alterations, achieved by modifying the underside of the shoe. The data were recorded at 100 Hz over 2520 data channels and was analysed using the deep learning architecture, long term short term memory networks. Additionally, the rationale for the decision-making process of these networks was investigated. Conclusion: Long term short term memory networks are applicable to the classification of gait function. The classifications can be made using only 2 seconds of sparse data (82.0% accuracy over 96,000 instances of test data) from participants who were not part of the training set. Significance: This work provides potential for gait function to be accurately classified using non-invasive techniques, and at more regular intervals, outside of a clinical setting without the need for healthcare professionals to be present.

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Section: A Related Workmentioning

confidence: 99%

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning

Turner

Hayes

2019

IEEE Trans. Biomed. Eng.

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“…In the past decade, smart sensors, especially wearable ones have increasingly become a tool for assessment of motor symptoms such as FOG in PD and other movement disorders. This is because of improvements in computational power of small devices [12]. The proposed methods for FOG detection using these sensors are categorized into two groups depending on the type of the signal and the analysis method used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…DL models learn feature extractions that can easily handle multimodal data, missing information, and high-dimensional feature spaces. Just a few papers are proposed for FOG detection using DL including [12,32]. Julia et al [32] proposed a DL model which uses a 6-layer CNN network for FOG detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Soltaninejad

Cheng

Basu

2019

Sensors

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Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

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“…specific in step separation, position and swing stages which is 94.4%, 77.8% and 86.1%. In [15] the researchers recommended that FOG recognition is performed utilizing profound learning. The specialists utilized wearable unit over midsection of subjects comprising tripivotal accelerometer, gyroscope and magnetometer.…”

Section: Related Workmentioning

confidence: 99%

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

Channa¹,

Baqai²,

Ceylan³

2019

IJACSA

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For analysis of Parkinson illness gait disabilities detection is essential. The only motivation behind this examination is to equitably and consequently differentiate among sound subjects and the one who is forbearing the Parkinson, utilizing IOT based indicative framework. In this examination absolute, 16 distinctive force sensors being attached with the shoes of subjects which documented the Multisignal Vertical Ground Reaction Force (VGRF). Overall sensors signals utilizing 1024 window estimate around the raw signals, utilizing the Packet wavelet change (PWT) five diverse characteristics that includes entropy, energy, variance, standard deviation and waveform length were derived and support vector machine (SVM) is to recognize Parkinson patients and healthy subjects. SVM is trained on 85% of the dataset and tested on 15% dataset. Preparation accomplice relies upon 93 patients with idiopathic PD (mean age: 66.3 years; 63% men and 37% ladies), and 73 healthy controls (mean age: 66.3 years; 55% men and 45% ladies). IOT framework included all 16 sensors, from which 8 compel sensors were appended to left side foot of subject and the rest of the 8 on the right side foot. The outcomes demonstrate that fifth sensor worn on a Medial part of the dorsum of right foot highlighted by R5 gives 90.3% accuracy. Henceforth this examination gives the knowledge to utilize single wearable force sensor. Hence, this examination deduce that a solitary sensor might help in differentiation amongst Parkinson and healthy subjects.

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Deep Learning for Detecting Freezing of Gait Episodes in Parkinson’s Disease Based on Accelerometers

Cited by 25 publications

References 16 publications

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

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