Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Fall prevention strategy for an active orthotic system

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)Todos os anos, são reportadas cerca de 684,000 quedas fatais e 37.3 milhões de quedas não fatais que requerem atenção médica, afetando principalmente a população idosa. Assim, é necessário identificar eficientemente indivíduos com alto risco de queda, a partir da população alvo idosa, e prepará los para superar perturbações da marcha inesperadas. Uma estratégia de prevenção de queda capaz de eficientemente e atempadamente detetar e contrariar os eventos de perdas de equilíbrio (PDE) mais frequentes pode reduzir o risco de queda. Como slips foram identificados como a causa mais prevalente de quedas, estes eventos devem ser abordados como foco principal da estratégia. No entanto, há falta de estratégias de prevenção de quedas por slip. Esta dissertação tem como objetivo o design de uma estratégia de prevenção de quedas de slips baseada na conceção das etapas de atuação e deteção. A estratégia de atuação foi delineada com base na resposta biomecânica humana a slips, onde o joelho da perna perturbada (leading) apresenta um papel proeminente para contrariar LOBs induzidas por slips. Quando uma slip é detetada, a estratégia destaca uma ortótese de joelho que providencia um torque assisstivo para prevenir a queda. A estratégia de deteção considerou as propriedades atrativas dos controladores Central Pattern Generator (CPG) para prever parâmetros da marcha. Algoritmos baseados em threshold monitorizam o erro de previsão do CPG, que aumenta após uma perturbação inesperada na marcha, para a deteção de slips. O ângulo do joelho e a velocidade angular da canela foram selecionados como os parâmetros de monitorização da marcha. Um protocolo experimental concebido para provocar perturbações de slip a sujeitos humanos permitiu a recolha de dados destas variáveis para posteriormente validar o algoritmo de deteção de perturbações. Algoritmos CPG foram capazes de produzir aproximações aceitáveis dos sinais de marcha em estado estacionário do ângulo do joelho e da velocidade angular da canela com sucesso. Além disso, o algoritmo de threshold adaptativo detetou LOBs induzidas por slips eficientemente. A melhor performance global foi obtida usando este algoritmo para monitorizar o ângulo do joelho, que detetou quase 80% (78.261%) do total de perturbações com um tempo médio de deteção (TMD) de 250 ms. Além disso, uma média de 0.652 falsas perturbações foram detetadas por cada perturbação corretamente identificada. Estes resultados sugerem uma performance aceitável de deteção de perturbações do algoritmo, de acordo com os requisitos especificados para a deteção.Every year, an estimated 684,000 fatal falls and 37.3 million non-fatal falls requiring medical attention are reported, mostly affecting the older population. Thus, it is necessary to effectively screen high fall risk individuals from targeted elderly populations and prepare them to successfully overcome unexpected gait perturbations. A fall prevention strategy capable of effectively and timely detect and counteract the most frequent loss of balance (LOB) events may reduce the fall risk. Since slips were identified as the main contributors to falls, these events should be addressed as a main focus of the strategy. Nonetheless, there is a lack of slip-induced fall prevention strategies. This dissertation aims the design of a slip-related fall prevention strategy based on the conception of an actuation and a detection stage. The actuation strategy was delineated based on the human biomechanical reactions to slips, where the perturbed (leading) leg’s knee joint presents a prominent role to counteract slip-induced LOBs. Thereby, upon the detection of a slip, this strategy highlighted a knee orthotic device that provides an assistive torque to prevent the falls. The detection strategy considered the attractive properties of biological-inspired Central Pattern Generator (CPG) controllers to predict gait parameters. Threshold-based algorithms monitored the CPG’s prediction error produced, which increases upon an unexpected gait perturbation, to perform slip detection. The knee angle and shank angular velocity were selected as the monitoring gait parameters. An experimental protocol designed to provoke slip perturbations to human subjects allowed to collect data from these variables to further validate the perturbation detection algorithm. CPG algorithms were able to successfully produce acceptable estimations of the knee angle and shank angular velocity signals during steady-state walking. Furthermore, an adaptive threshold algorithm effectively detected slip-induced LOBs. The best overall performance was obtained using this algorithm to monitor the knee angle from the perturbed leg, which detected almost 80% (78.261%) of the total perturbations with a mean detection time (MDT) of 250 ms. In addition, a mean of 0.652 false perturbations were detected for each correct perturbation identified. These results suggest an acceptable perturbation detection performance of the algorithm implemented in light of the detection requirements specified

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Development of a Fall Risk Asessment Tool Using Gait Analysis

In the United States, falls are one of the leading causes of fatal and non-fatal injuries for people of all ages. Current clinical methods to assess fall risk are impractical, and often do not use individuals’ actual performance. With current technological advances, and the Internet of Things (IoT), the tools are available to create a digital system that can take into account an individual’s actual performance in making a fall risk assessment. A digital insole based sensory computing system can collect and analyze human gait patterns to develop a fall risk assessment platform with great accuracy.The presented research considers current clinical methods and describes a computerized self-service platform that successfully addresses different gait variables and metrics critical to accurate fall risk assessment. The system incorporates a shoe insole with pressure sensors, and an accelerometer. Collected foot data are transferred to an analytics visualization platform. A wide range of gait pattern recognition metrics, and gait data analyses features are then displayed on the platform enabling specific fall risk assessment

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Identifying Gait Deficits in Stroke Patients Using Inertial Sensors

Falls remain a significant problem for stroke patients. Tripping, the main cause of falls, occurs when there is insufficient clearance between the foot and ground. Based on an individual’s gait deficits, different joint angles and coordination patterns are necessary to achieve adequate foot clearance during walking. However, gait deficits are typically only quantified in a research or clinical setting, and it would be helpful to use wearable devices – such as accelerometers – to quantify gait disorders in real-world situations. Therefore, the objective of this project was to understand gait characteristics that influence the risk of tripping, and to detect these characteristics using accelerometers. Thirty-five participants with a range of walking abilities performed normal walking and attempted to avoid tripping on an unexpected object while gait characteristics were quantified using motion capture techniques and accelerometers. Multiple regression was used to identify the relationship between joint coordination and foot clearance, and multiple analysis of variance was used to determine characteristics of gait that differ between demographic groups, as well as those that enable obstacle avoidance. Machine learning techniques were employed to detect joint angles and the risk of tripping from patterns in accelerometer signals. Measures of foot clearance that represent toe height throughout swing instead of at a single time point are more sensitive to changes in joint coordination, with hip-knee coordination during midswing having the greatest effect. Participants with a history of falls or stroke perform worse than older non-fallers and young adults on many factors related to falls risk, however, there are no differences in the ability to avoid an unexpected obstacle between these groups. Individuals with an inability to avoid an obstacle have lower scores on functional evaluations, exhibit limited sagittal plane joint range of motion during swing, and adopt a conservative walking strategy. Machine learning processes can be used to predict knee range of motion and classify individuals at risk for tripping based on an ankle-worn accelerometer. This work is significant because a portable device that detects gait characteristics relevant to the risk of tripping without expensive motion capture technology may reduce the risk of falls for stroke patients

A study on detection of risk factors of a toddler’s fall injuries using visual dynamic motion cues

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The research in this thesis is intended to aid caregivers’ supervision of toddlers to prevent accidental injuries, especially injuries due to falls in the home environment. There have been very few attempts to develop an automatic system to tackle young children’s accidents despite the fact that they are particularly vulnerable to home accidents and a caregiver cannot give continuous supervision. Vision-based analysis methods have been developed to recognise toddlers’ fall risk factors related to changes in their behaviour or environment. First of all, suggestions to prevent fall events of young children at home were collected from well-known organisations for child safety. A large number of fall records of toddlers who had sought treatment at a hospital were analysed to identify a toddler’s fall risk factors. The factors include clutter being a tripping or slipping hazard on the floor and a toddler moving around or climbing furniture or room structures. The major technical problem in detecting the risk factors is to classify foreground objects into human and non-human, and novel approaches have been proposed for the classification. Unlike most existing studies, which focus on human appearance such as skin colour for human detection, the approaches addressed in this thesis use cues related to dynamic motions. The first cue is based on the fact that there is relative motion between human body parts while typical indoor clutter does not have such parts with diverse motions. In addition, other motion cues are employed to differentiate a human from a pet since a pet also moves its parts diversely. They are angle changes of ellipse fitted to each object and history of its actual heights to capture the various posture changes and different body size of pets. The methods work well as long as foreground regions are correctly segmented

Dense & Attention Convolutional Neural Networks for Toe Walking Recognition

Idiopathic toe walking (ITW) is a gait disorder where children’s initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are incorporated into the network to highlight useful features while suppressing unwanted noises. Also, the Focal Loss function is enhanced to alleviate the imbalance sample issue. The proposed approach outperforms other methods and obtains a superior performance. It achieves a test recall of 88.91% for recognizing idiopathic toe walking on the local dataset collected from real-world experimental scenarios. To ensure the scalability and generalizability of the proposed approach, the algorithm is further validated through the publicly available datasets, and the proposed approach achieves an average precision, recall, and F1-Score of 89.34%, 91.50%, and 92.04%, respectively. Experimental results present a competitive performance and demonstrate the validity and feasibility of the proposed approach

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Automatic fall detection is one of the most promising applications of wearables in the field of mobile health. The characterization of the effectiveness of wearable fall detectors is hampered by the inherent difficulty of testing these devices with real-world falls. In fact, practically all the proposals in the literature assess the detection algorithms with ‘scripted’ falls that are simulated in a controlled laboratory environment by a group of volunteers (normally young and healthy participants). Aiming at appraising the adequacy of this method, this work systematically compares the statistical characteristics of the acceleration signals from two databases with real falls and those computed from the simulated falls provided by 18 well-known repositories commonly employed by the related works. The results show noteworthy differences between the dynamics of emulated and real-life falls, which undermines the testing procedures followed to date and forces to rethink the strategies for evaluating wearable fall detectors.Funding for open access charge: Universidad de Málaga / CBUA. This research was funded by FEDER Funds (under grant UMA18-FEDERJA-022), Andalusian Regional Government (-Junta de Andalucía- grant PAIDI P18-RT-1652) and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech