What Can Quantitative Gait Analysis Tell Us about Dementia and Its Subtypes? A Structured Review

Distinguishing dementia subtypes can be difficult due to similarities in clinical presentation. There is increasing interest in discrete gait characteristics as markers to aid diagnostic algorithms in dementia. This structured review explores the differences in quantitative gait characteristics between dementia and healthy controls, and between four dementia subtypes under single-task conditions: Alzheimer’s disease (AD), dementia with Lewy bodies and Parkinson’s disease dementia, and vascular dementia. Twenty-six papers out of an initial 5,211 were reviewed and interpreted using a validated model of gait. Dementia was associated with gait characteristics grouped by slower pace, impaired rhythm, and increased variability compared to normal aging. Only four studies compared two or more dementia subtypes. People with AD are less impaired in pace, rhythm, and variability domains of gait compared to non-AD dementias. Results demonstrate the potential of gait as a clinical marker to discriminate between dementia subtypes. Larger studies using a more comprehensive battery of gait characteristics and better characterized dementia sub-types are required

Analysis of the complexity and variability of fine and gross motor tasks in fibromyalgia patients: precision and retrospective cross-sectional studies

Fibromyalgia (FM) can be defined as a non-inflammatory chronic and widespread pain disease (Gentile et al., 2019) that present and series of other symptoms such as fatigue, Allodynia, Hyperalgesia, functional impairment, balance deficit, and others (ACSM, 2021; Rasouli et al., 2017). FM is considered to be a disease or syndrome that shows a central nervous system dysfunction in pain modulation (Gentile et al., 2019). This functional impairment in FM patients may be related to disturbances in motor functions, such as deficits in fine and gross motor control (Pérez-de-Heredia-Torres et al., 2013; Rasouli et al., 2017). Until today, it is still impossible to confirm the diagnosis of Fibromyalgia because no clinical tests are available for this purpose (ACSM, 2021). The present dissertation intends to verify if Inertial Measurement Units (IMU) are instruments that can facilitate the applicability (Study 1) of FTT; Analyze and interpret entropy values during fine and gross motor control tasks (Study 2), and assess the variability during the same fine and gross motor control tasks (Study 3) of individuals with FM diagnosis; and also to verify if the IMU with the non-linear analysis can characterize FM patients. The sample of 20 female subjects, 10 with FM and 10 without, with ages between 20 and 70 years old, was divided into experimental and control groups. Participants were asked to perform de finger tapping test with both hands, the gait task, and the sit and stand test. IMUs were used in all tasks to collect the required data for each study. Non linear measures of entropy and variability were used to allow a detailed and deeper motor control analysis, focusing on the process and on the quality of movement (Azami et al., 2017). The results showed that using inertial sensors may be of great applicability in the finger tapping test, and it could be a possible alternative to the traditional method. This method allows the tridimensional collection and analysis of other important information that we can only access by looking at the process and not just the results in a more practical, faster, and cheaper way. And the use of IMU, along with non-linear analysis in fine and gross motor control, could allow a better understanding and characterization of both groups, Fibromyalgia, and control, through the analysis of entropy and variability In conclusion, the use of inertial sensors to collect data from fine and gross motor has great potential and brings innovation to exercise researchers and professionals.A fibromialgia (FM) pode ser definida como uma doença não inflamatória com dor crónica generalizada (Gentile et al., 2019), e que apresenta uma série de outros sintomas como a fadiga, alodinia, hiperalgesia, comprometimento funcional, deficits de equilíbrio, entre outros (ACSM, 2021; Rasouli et al., 2017). A FM é considerada uma doença ou síndrome que apresenta uma disfunção por parte do sistema nervoso central no processamento e regulação da dor (Gentile et al., 2019). Esse comprometimento funcional em pacientes com FM pode estar relacionado com a presença de distúrbios motores, como deficits na motricidade fina e grossa (Pérez-de-Heredia-Torres et al., 2013; Rasouli et al., 2017). Até hoje ainda não é possível confirmar o diagnóstico de fibromialgia, pois não existem testes clínicos disponíveis para o efeito (ACSM, 2021). A presente dissertação pretende verificar se os sensores inerciais (IMUs) são instrumentos que podem facilitar a aplicação (Estudo 1) do FTT; analisar e interpretar valores de entropia durante a realização de tarefas de motricidade fina e grossa (Estudo 2) e, analisar a variabilidade durante a execução das mesmas tarefas de controlo motor fino e grosso de indivíduos com FM, e verificar se o IMU juntamente com a análise não linear, permite uma caracterização da fibromialgia. A amostra desta dissertação é constituída por 20 sujeitos do sexo feminino, 10 com FM e 10 sem FM, com idades compreendidas entre os 20 e os 70 anos, divididos em dois grupos, grupo experimental e grupo de controlo, respetivamente. Foi solicitado aos participantes que realizassem três tarefas motoras: o finger tapping test em ambas as mãos, a marcha e o teste de sentar-e-levantar. Os IMUs foram utilizados em todas as tarefas para recolher os dados necessários para cada estudo, de modo a serem aplicadas medidas de análise não-linear de entropia e variabilidade. Este tratamento de dados foi utilizado para permitir uma análise mais detalhada e profunda do controlo do movimento, com principal foco no processo e na qualidade do movimento (Azami et al., 2017). Os resultados desta dissertação mostraram que a utilização de sensores inerciais parece ter uma grande aplicabilidade no teste de finger tapping, e que o mesmo pode ser uma possível alternativa ao método validado. O IMU permite uma recolha e análise tridimensional, o qual possibilita entender o processo de controlo do movimento e não apenas o resultado, fazendo-o de forma mais prática, rápida e económica. O uso de IMUs juntamente com análises não-lineares na motricidade fina e grossa pode permitir uma melhor compreensão e caracterização de ambos os grupos, fibromialgia e controlo, através da análise da entropia e da variabilidade. Em conclusão, o uso de sensores inerciais apresenta um grande potencial e traz inovação para investigadores e profissionais do exercício.N/

the wearable devices opportunity

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2022. 8. 김기웅.배경 및 목적: 치매로 인한 공공보건 부담이 가중됨에도 만족스러운 치료법은 부재한 현 상황은 치매 발병을 예방하거나 진행을 지연시키기 위해 인지저하 또는 치매 위험이 있는 사람들을 조기에 식별해야 할 필요를 더욱 부각시킨다. 최근 연구들은 보행 시 한발-한발 사이 보행인자들의 변동성을 의미하는 보행변이성이 인지 저하, 경도인지장애 및 치매의 위험과 밀접하게 관련되어 있다는 것을 보였다. 특히 웨어러블 센서를 통해 얻은 보행변이성은 감독이 없는 자연스러운 환경에서 더 오랜 기간 동안 측정값을 낮은 비용으로 얻을 수 있다는 실용적인 이점으로 인해 인지저하의 위험을 예측하는 유망한 디지털 바이오마커로 활용될 수 있다. 본 논문에서는, 신체에 부착한 단일 삼축가속계로 측정된 보행 변이성이 미래의 인지저하 위험을 예측하는 디지털 바이오마커로 사용될 수 있을지에 대해 연구하였다. 첫 번째 연구에서는 신체 부착 삼축가속계로 얻은 보행변이성이 정상인지를 가진 노인에서 미래 인지저하의 위험을 예측할 수 있는지를 조사했다. 두 번째 연구에서는 더 큰 표본 크기와 더 넓은 범위의 인지 기능을 가진 비치매 노인을 대상으로, 디지털 바이오마커로서 보행 변이성의 가능성을 이론적으로 뒷받침할 수 있는 신경기질에 대해 조사하였다. 또한, 높은 보행 변이성이 인지 기능 및 기억 기능에 관련된 것으로 밝혀진 뇌 영역에서의 얇아진 대뇌 피질 두께와 관련되어 있을 것이며, 그 영역이 보행-인지 사이의 연관성을 설명하는 공유 신경 기질에 해당할 것이라는 가설을 검증하였다. 방법: 연구 I에서 우리는 뇌허혈이나 파킨슨병이 없으면서, 지역사회에 거주하는, 인지적으로 정상인 노인 91명을 대상으로 4년 전향적 코호트 연구를 수행하였다. 체중심에 부착한 삼축가속계를 이용하여 보행변이성을 측정하였고, 경도인지장애에 관한 국제 워킹 그룹의 진단기준에 따라 경도인지 장애를 진단했다. 우리는 보행 변이성의 크기에 따라 연구대상자를 삼분위수로 분류하여, 보행변이성이 가장 큰 일분위 그룹과 나머지 그룹을 관찰하며 4년 동안의 경도인지장애의 발병을 추적하였다. 그룹간 경도인지장애 발병 위험 비교는 Log-rank test와 Kaplan-Meier 분석을 통해 수행했다. 경도인지장애 발병 위험비(Hazard Ratio, HR)는 연령, 성별, 교육수준, 누적질병평가척도 점수, GDS 점수, 아포지단백 E ε4 대립유전자 유무를 보정한 콕스 비례위험 회귀 분석을 사용하여 추정하였다. 연구 II에서 우리는 207명의 치매가 없는 노인을 대상으로, 보행변이성과 연관된 뇌 피질 및 피질 하 신경 구조, 보행변이성-인지기능의 공유신경기질을 횡단적으로 연구하였다. 자기공명영상에서 뇌 피질의 두께와 피질 하 구조물 부피를 구하여 보행변이성, 인지기능, 피질 두께와 피질 하 구조물 부피와의 연관성을 각각 조사했다. 또한 보행변이성과 인지기능 양쪽에 모두 유의한 연관성을 보이는 뇌영역의 피질 두께 또는 피질 하 구조물 부피가 실제로 보행변이성과 인지기능 관계에 미치는 매개효과를 분석하였다. 결과: 연구 I에서 보행변이성이 일분위에 속하는 노인들은 나머지 노인들에 비해서 4년 간 경도인지장애 발병 위험이 약 12배 더 높았다. (HR = 11.97, 95% CI = 1.29–111.37). 그러나 느린 보행 속도를 가진 노인들은 나머지 노인들과 비슷한 경도인지장애 발병위험을 보였다. (HR = 5.04, 95% CI = 0.53–48.18). 우리는 또한 보행변이성이 미래 인지저하를 예측하는 것에는 성별에 따른 차이가 유의하지 않다는 것을 밝혔다. 연구 대상자들을 보행변이성의 크기로 삼분위화 하는 과정에서의 역치효과 (threshold effect) 유무를 알아보기 위해, 보행변이성과 보행속도를 연속변수로 두고 분석하였을 때에도 보행변이성이 10% 증가할 때마다 인지저하의 위험이 1.5배 증가하는 반면 보행속도의 변화에 따라 인지감퇴 위험의 유의한 변화는 없었다. 연구 II에서 높은 보행변이성은 낮은 인지기능과 관련이 있었다. 우리는 높은 보행변이성이 광범위한 영역에서 대뇌피질 두께 감소와 연관이 있다는 것을 확인했다. 반면, 보행변이성은 피질 하 구조물의 부피와는 유의한 연관성을 보이지 않았다. 보행변이성과 유의한 상관관계를 보인 피질 클러스터 중 좌반구의 inferior temporal, entorhinal, parahippocampal, fusiform, and lingual을 포함하는 클러스터의 피질 두께는 전반적 인지기능 및 언어기억기능과 연관이 있었다. 결론 및 해석: 결론적으로 본 연구는, 신체부착 단일 삼축가속계로 측정한 보행변이성의 인지저하 위험 예측 디지털 바이오마커로서의 가능성에 근거를 제시하고 있다.Background and Objectives: Large public health burden of dementia and the absence of a cure highlight the need for early identification of those at risk for cognitive decline or dementia to prevent and/or delay the onset of dementia. Emerging evidence indicates gait variability, the fluctuation of a gait measure from one step to the next, strongly relate to the risk of cognitive decline, MCI and dementia. Gait variability obtain via wearable sensor is a promising digital biomarker for predicting risk of cognitive impairment due to its favorable practical advantages of being able to obtain measurements over a longer period of time under unsupervised real-world conditions at lower cost. In my thesis, I examine the possibility that gait variability measured by a single body-worn tri-axial accelerometer (TAA) can be used as a digital biomarker to predict future risk of cognitive decline. In the first study, I examined whether gait variability obtained by the body-worn TAA could predict future risk of cognitive decline in older people with normal cognition (NC). In the second study, I then identify neural substrates that theoretically support the potential of gait variability as a digital biomarker in older adults with larger sample size and broader range of cognitive function. Additionally, I hypothesized higher gait variability would be related to lower cortical thickness, especially in regions important for cognitive function and memory, and that these regions would represent a shared neural substrate for gait control and cognitive impairment. Methods: In the study I, we conducted 4-year prospective cohort study on 91 community-dwelling cognitively normal elderly individuals without cerebral ischemic burden or Parkinsonism. We evaluated gait speed and step time variability using a TAA placed on the center of body mass, and diagnosed mild cognitive impairment (MCI) according to the International Working Group on MCI. We performed Kaplan-Meier analysis with consecutive log-rank testing for MCI-free survival by cohort-specific tertiles of gait variability; hazard ratios (HR) of incident MCI were estimated using Cox proportional hazards regression analysis adjusted for age, sex, education level, Cumulative Illness Rating Scale score, GDS score, and presence of the apolipoprotein E ε4 allele. In the study II, we cross-sectionally investigated the cortical and subcortical neural structures associated with gait variability, and the shared neural substrates of gait variability and cognitive function in 207 non-demented older adults. We obtained the cortical thickness and subcortical volumes from the magnetic resonance images, and examined associations between gait variability, cognitive function, and cortical thickness and subcortical volumes. Finally, we analyzed the mediation effect of the cluster cortical thickness and subcortical volume which had a significant association with both gait variability and cognitive function on the association between gait variability and cognition. Results: In the study I, subjects with high gait variability showed about 12-fold higher risk of MCI (HR = 11.97, 95% CI = 1.29–111.37) than those with mid-to-low variability. However, those with slow gait speed showed comparable MCI risk to those with mid-to-high speed (HR = 5.04, 95% CI = 0.53– 48.18). We additionally found that no sex differences were found when assessing the ability of high gait variability to predict future cognitive decline. When we computed gait variability and gait speed as continuous variables to explore whether there are any threshold effects, the risk of incident cognitive decline increased 1.5 times per 10% increment of gait variability, whereas it did not change significantly with changes of gait speed. In the study II, higher gait variability was associated with lower cognitive functions. We found the widespread decrease in cortical thickness with increasing gait variability while there was no significant association with the volume of subcortical structures. Among the clusters that showed significant correlation with the gait variability, a cluster that included the inferior temporal, entorhinal, parahippocampal, fusiform, and lingual in left hemisphere was also associated with global cognitive function, and verbal memory function. Cortical thickness of the cluster explained 17% of the total effect of gait variability on global cognitive function measured by CERAD-TS. Interpretation: Gait variability measured by a single body-worn TAA could be a novel digital biomarker of risk of cognitive decline that could be used repeatedly and frequently and at low cost to test risk of individuals without clinical evidence of cognitive impairments.I Introduction 10 1. Study background 11 2. Purpose of research 16 II Methods 18 1. Study 1: Can gait variability predict the risk of cognitive decline in cognitively normal elderly? 19 1.1. Study population 19 1.2. Clinical assessments 20 1.3. Gait Assessments 22 1.4. Statistical analysis 23 2. Study 2: Shared Neural Substrates between Gait Variability-Cognitive Function 24 2.1. Study population 24 2.2. Assessments of cognition and medical conditions 25 2.3. Gait assessments 26 2.4. Magnetic resonance imaging (MRI) acquisition and preprocessing 27 2.5. Statistical analyses 28 III Results 32 1. Study 1: Can gait variability predict the risk of cognitive decline in cognitively normal elderly? 33 1.1. Association of gait variability and gait speed status with the risk of MCI 34 2. Study 2: Shared Neural Substrates between Gait Variability-Cognitive Function 35 IV Discussion 38 [Figure 1] Risk of incident mild cognitive impairment over 4 years stratified by gait speed (a) and variability (b) by log-rank test 49 [Figure 2] Cortical thickness and gait variability in non-demented older adults 50 [Figure 3] Cortical thickness of LH1 cluster mediates effect of gait variability on CERAD-TS (a) and VMS (b) 51 [Table 1] Demographic, clinical, cognitive function, and gait characteristics of the subjects 52 [Table 2] Prediction of mild cognitive impairment in cognitively normal elderly individuals 54 [Table 3] Characteristics of participants 55 [Table 4] Vertex-Wise Analyses of Gait Variability and Cortical Thickness 56 [Table 5] Regression Analyses of Gait Variability and Cortical Thickness 57 [Table 6] Associations between Cortical Regions related with Gait Variability and Cognitive Function 58 Bibliography 59 감사의 글 66 초 록 67박

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Protocol for PD SENSORS:Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcomes measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

Introduction The impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson’s disease.Methods and analysis This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ and ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.Ethics and dissemination Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate

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

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

Fall risk in the aging population: fall prevention using smartphones technology and multiscale sample entropy

Falls are an important aspect of older people's health because they trigger major injuries and even death in one-third of fallen patients, making them  a major public health problem. Given the risk of physical and psychological injury, if serious injuries occur as a result of a fall, prevention is an important consideration in today's health care landscape, where the population is predominantly adult world wide. This paper presents the applicability ofa simple technique of analysis of gait signals capturedby mobile devices with the objective to the generation of early warnings on the risk of falls in older adults, which correlates with subjective scales. The technique is tested in a population of patients showing results of the significant risk of falls inpatients that the subjective scales could not detect, demonstrating that mobile devices and signal processing can become important tools in the service of elderly care in fall risk prevention

Detection of Alzheimer’s Disease in Elder People Using Gait Analysis and Kinect Camera

Introduction: Gait analysis using modern technology for detection of Alzheimer's disease has found special attention by researchers over the last decade. In this study, skeletal data recorded with a Kinect camera, were used to analyze gait for the purpose of detecting Alzheimer's disease in elders. Method: In this applied-developmental experimental study, using a Kinect camera, data were collected for 12 elderly women with Alzheimer's disease and 12 healthy elderly women walking in an oval path. After extracting various features of gait, descriptive analysis was performed to compare the features between the healthy and patient groups. Then, a support vector machine classifier was designed to detect elderly people with Alzheimer's disease. Results: The comparison of extracted features from skeletal data of gait using Kinect camera in this study indicate that the results are matched with previous findings from systems based on other types of sensors. The accuracy, sensitivity, precision and specificity of system designed in the present study for classifying elders with Alzheimer's disease and healthy elders were 91.25%, 93.4484%, 90.5945% and 93.581% respectively. Conclusion: In addition to descriptive analysis of gait, by using machine learning methods such as support vector machine classifier, elderly people with Alzheimer's disease can be detected based on features extracted from skeletal data of Elderly people