79 research outputs found

    Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition

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    Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentially classify this data. The combination therefore holds the potential to detect FOG in real-time. In this work I present a simple 1-D convolutional neural network that was trained to detect FOG events in accelerometer data. Model performance was assessed by measuring the success of the model to discriminate normal movement from FOG episodes and resulted in a mean average precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction competition. The results underscore the potential of Deep Learning-based solutions in advancing the field of FOG detection, contributing to improved interventions and management strategies for PD patients.Comment: 5 pages, 2 figures, competition report, for associated code see: https://github.com/janbrederecke/fo

    Wearables for independent living in older adults: Gait and falls

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    Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults 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 the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating 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. Suggestions for how this domain needs to progress are also summarised

    Deep Learning Based Abnormal Gait Classification System Study with Heterogeneous Sensor Network

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    Gait is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion site and has been demonstrated to play a guiding role in clinical research such as medical diagnosis and disease prevention. In order to promote the research of automatic gait pattern recognition, this paper introduces the research status of abnormal gait recognition and systems analysis of the common gait recognition technologies. Based on this, two gait information extraction methods, sensor-based and vision-based, are studied, including wearable system design and deep neural network-based algorithm design. In the sensor-based study, we proposed a lower limb data acquisition system. The experiment was designed to collect acceleration signals and sEMG signals under normal and pathological gaits. Specifically, wearable hardware-based on MSP430 and upper computer software based on Labview is designed. The hardware system consists of EMG foot ring, high-precision IMU and pressure-sensitive intelligent insole. Data of 15 healthy persons and 15 hemiplegic patients during walking were collected. The classification of gait was carried out based on sEMG and the average accuracy rate can reach 92.8% for CNN. For IMU signals five kinds of abnormal gait are trained based on three models: BPNN, LSTM, and CNN. The experimental results show that the system combined with the neural network can classify different pathological gaits well, and the average accuracy rate of the six-classifications task can reach 93%. In vision-based research, by using human keypoint detection technology, we obtain the precise location of the key points through the fusion of thermal mapping and offset, thus extracts the space-time information of the key points. However, the results show that even the state-of-the-art is not good enough for replacing IMU in gait analysis and classification. The good news is the rhythm wave can be observed within 2 m, which proves that the temporal and spatial information of the key points extracted is highly correlated with the acceleration information collected by IMU, which paved the way for the visual-based abnormal gait classification algorithm.步态指人走路时表现出来的姿态,是人体重要生物特征之一。异常步态多与病变部位有关,作为反映人体健康状况和行为能力的重要特征,其被论证在医疗诊断、疾病预防等临床研究中具有指导作用。为了促进步态模式自动识别的研究,本文介绍了异常步态识别的研究现状,系统地分析了常见步态识别技术以及算法,以此为基础研究了基于传感器与基于视觉两种步态信息提取方法,内容包括可穿戴系统设计与基于深度神经网络的算法设计。 在基于传感器的研究中,本工作开发了下肢步态信息采集系统,并利用该信息采集系统设计实验,采集正常与不同病理步态下的加速度信号与肌电信号,搭建深度神经网络完成分类任务。具体的,在系统搭建部分设计了基于MSP430的可穿戴硬件设备以及基于Labview的上位机软件,该硬件系统由肌电脚环,高精度IMU以及压感智能鞋垫组成,该上位机软件接收、解包蓝牙数据并计算出步频步长等常用步态参数。 在基于运动信号与基于表面肌电的研究中,采集了15名健康人与15名偏瘫病人的步态数据,并针对表面肌电信号训练卷积神经网络进行帕金森步态的识别与分类,平均准确率可达92.8%。针对运动信号训练了反向传播神经网络,LSTM以及卷积神经网络三种模型进行五种异常步态的分类任务。实验结果表明,本工作中步态信息采集系统结合神经网络模型,可以很好地对不同病理步态进行分类,六分类平均正确率可达93%。 在基于视觉的研究中,本文利用人体关键点检测技术,首先检测出图片中的一个或多个人,接着对边界框做图像分割,接着采用全卷积resnet对每一个边界框中的人物的主要关节点做热力图并分析偏移量,最后通过热力图与偏移的融合得到关键点的精确定位。通过该算法提取了不同步态下姿态关键点时空信息,为基于视觉的步态分析系统提供了基础条件。但实验结果表明目前最高准确率的人体关键点检测算法不足以替代IMU实现步态分析与分类。但在2m之内可以观察到节律信息,证明了所提取的关键点时空信息与IMU采集的加速度信息呈现较高相关度,为基于视觉的异常步态分类算法铺平了道路

    Optimization Algorithms for Integrating Advanced Facility-Level Healthcare Technologies into Personal Healthcare Devices

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    Healthcare is one of the most important services to preserve the quality of our daily lives, and it is capable of dealing with issues such as global aging, increase in the healthcare cost, and changes to the medical paradigm, i.e., from the in-facility cure to the prevention and cure outside the facility. Accordingly, there has been growing interest in the smart and personalized healthcare systems to diagnose and care themselves. Such systems are capable of providing facility-level diagnosis services by using smart devices (e.g., smartphones, smart watches, and smart glasses). However, in realizing the smart healthcare systems, it is very difficult, albeit impossible, to directly integrate high-precision healthcare technologies or scientific theories into the smart devices due to the stringent limitations in the computing power and battery lifetime, as well as environmental constraints. In this dissertation, we propose three optimization methods in the field of cell counting systems and gait-aid systems for Parkinson's disease patients that address the problems that arise when integrating a specialized healthcare system used in the facilities into mobile or wearable devices. First, we present an optimized cell counting algorithm based on heuristic optimization, which is a key building block for realizing the mobile point-of-care platforms. Second, we develop a learning-based cell counting algorithm that guarantees high performance and efficiency despite the existence of blurry cells due to out-focus and varying brightness of background caused by the limitation of lenses free in-line holographic apparatus. Finally, we propose smart gait-aid glasses for Parkinson’s disease patients based on mathematical optimization. ⓒ 2017 DGISTopenI. Introduction 1-- 1.1 Global Healthcare Trends 1-- 1.2 Smart Healthcare System 2-- 1.3 Benefits of Smart Healthcare System 3-- 1.4 Challenges of Smart Healthcare. 4-- 1.5 Optimization 6-- 1.6 Aims of the Dissertation 7-- 1.7 Dissertation Organization 8-- II.Optimization of a cell counting algorithm for mobile point-of-care testing platforms 9-- 2.1 Introduction 9-- 2.2 Materials and Methods. 13-- 2.2.1 Experimental Setup. 13-- 2.2.2 Overview of Cell Counting. 16-- 2.2.3 Cell Library Optimization. 18-- 2.2.4 NCC Approximation. 20-- 2.3 Results 21-- 2.3.1 Cell Library Optimization. 21-- 2.3.2 NCC Approximation. 23-- 2.3.3 Measurement Using an Android Device. 28-- 2.4 Summary 32-- III.Human-level Blood Cell Counting System using NCC-Deep learning algorithm on Lens-free Shadow Image. 33-- 3.1 Introduction 33-- 3.2 Cell Counting Architecture 36-- 3.3 Methods 37-- 3.3.1 Candidate Point Selection based on NCC. 37-- 3.3.2 Reliable Cell Counting using CNN. 40-- 3.4 Results 43-- 3.4.1 Subjects . 43-- 3.4.2 Evaluation for the cropped cell image 44-- 3.4.3 Evaluation on the blood sample image 46-- 3.4.4 Elapsed-time evaluation 50-- 3.5 Summary 50-- IV.Smart Gait-Aid Glasses for Parkinson’s Disease Patients 52-- 4.1 Introduction 52-- 4.2 Related Works 54-- 4.2.1 Existing FOG Detection Methods 54-- 4.2.2 Existing Gait-Aid Systems 56-- 4.3 Methods 57-- 4.3.1 Movement Recognition. 59-- 4.3.2 FOG Detection On Glasses. 62-- 4.3.3 Generation of Visual Patterns 66-- 4.4 Experiments . 67-- 4.5 Results 69-- 4.5.1 FOG Detection Performance. 69-- 4.5.2 Gait-Aid Performance. 71-- 4.6 Summary 73-- V. Conclusion 75-- Reference 77-- 요약문 89본 논문은 의료 관련 연구시설 및 병원 그리고 실험실 레벨에서 사용되는 전문적인 헬스케어 시스템을 개인의 일상생활 속에서 사용할 수 있는 스마트 헬스케어 시스템에 적용시키기 위한 최적화 문제에 대해 다룬다. 현대 사회에서 의료비용 증가 세계적인 고령화에 따라 의료 패러다임은 질병이 발생한 뒤 시설 내에서 치료 받는 방식에서 질병이나 건강관리에 관심있는 환자 혹은 일반인이 휴대할 수 있는 개인용 디바이스를 이용하여 의료 서비스에 접근하고, 이를 이용하여 질병을 미리 예방하는 방식으로 바뀌었다. 이에 따라 언제, 어디서나 스마트 디바이스(스마트폰, 스마트워치, 스마트안경 등)를 이용하여 병원 수준의 예방 및 진단을 실현하는 스마트 헬스케어가 주목 받고 있다. 하지만, 스마트 헬스케어 서비스 실현을 위하여 기존의 전문 헬스케어 장치 및 과학적 이론을 스마트 디바이스에 접목하는 데에는 스마트 디바이스의 제한적인 컴퓨팅 파워와 배터리, 그리고 연구소나 실험실에서 발생하지 않았던 환경적인 제약조건으로 인해 적용 할 수 없는 문제가 있다. 따라서 사용 환경에 맞춰 동작 가능하도록 최적화가 필요하다. 본 논문에서는 Cell counting 분야와 파킨슨 환자의 보행 보조 분야에서 전문 헬스케어 시스템을 스마트 헬스케어에 접목시키는데 발생하는 세 가지 문제를 제시하고 문제 해결을 위한 세 가지 최적화 알고리즘(Heuristic optimization, Learning-based optimization, Mathematical optimization) 및 이를 기반으로 하는 시스템을 제안한다.DoctordCollectio

    Prediction and Detection of Freezing of Gait in Parkinson's Disease using Plantar Pressure Data

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    Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting movement and is characterized by symptoms such as tremor, rigidity, and Freezing of Gait (FOG). FOG is a walking disturbance seen in more advanced stages of PD. FOG is characterized by the feeling of feet being glued to the ground and has been associated with higher risks of falls. While falling can have great repercussions in individuals with PD, leading to restricted movement and independence, hip fracture, and fatal injury, even the disturbance of FOG alone can lead to decreased mobility, inactivity, and decreased quality of life. Determining methods to counter FOG can potentially lead to a better life for people with PD (PwPD). Freezing episodes can be countered with the help of external intervention such as visual or auditory cues. Such intervention when administered during the freeze has been found to alleviate the freeze and thus prevent freeze-related falls. This sheds the importance of detecting or predicting a freeze event. Once a freeze is detected or predicted, an intervention can be administered to help prevent the freeze altogether (in case of prediction) or help resume normal walking (in case of detection). Different wearable sensors have been used to collect data from participants to understand FOG and develop approaches to detect and predict it. Plantar pressure data has earlier been used in gait related studies; however, they have not been used for FOG detection or prediction. Based on the hypothesis that plantar pressure data can capture subtle weight shifts unique to FOG episodes, this research aimed to determine if plantar pressure data alone can be used to detect and predict FOG. In this research, plantar (foot sole) pressure data were collected from shoe-insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path while on their normal antiparkinsonian medication. The sensors included IMU, EMG, and plantar pressure foot insoles; however, for the research in this thesis, only plantar pressure data were used. The walking trials were also video recorded for labelling the data. A custom-built application was used to synchronize data from all sensors and label them. This was followed by feature extraction, dataset balancing, and z-score normalization. The datasets generated were then classified using Long-short term memory (LSTM) networks. The best model had an average 82.0% (SD 6.25%) sensitivity and 89.4% (SD 3.60%) specificity for one-freezer-held-out cross validation tests. For the participants who did not freeze during the walking trials, an average 87.7% specificity was achieved. Since, FOG detection is done with the aim to provide an intervention, a freeze episode analysis was completed, and it was found that the model could correctly detect 95% of freeze episodes. The misclassified freezes and false positives were analyzed with respect to active (walking and turning) and inactive states (standing). The model’s specificity performance for one-freezer held out cross validation tests was found to improve to 93.3% when analyzing the model only on active states. FOG prediction was done afterwards, including data before FOG (labelled Pre-FOG) in the target class. The best FOG prediction method achieved an average 74.02% (SD 12.48%) sensitivity and 82.99% (SD 5.75%) specificity for one-freezer-held-out cross validation tests. The research showed that plantar pressure data can be successfully used for FOG detection and prediction. Moving away from window-based model also helped the research in reducing the freeze detection latency. However, further research is required to improve the FOG prediction performance and a bigger sample size should be used in future research

    Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

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    Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices

    Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

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    For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

    A deep learning approach for parkinson’s disease severity assessment

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    Purpose: Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved. Methods: We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest. Results: Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity. Conclusion: This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.publishedVersionPeer reviewe
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