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

A quantitative evaluation of physical and digital approaches to centre of mass estimation

Centre of mass is a fundamental anatomical and biomechanical parameter. Knowledge of centre of mass is essential to inform studies investigating locomotion and other behaviours, through its implications for segment movements, and on whole body factors such as posture. Previous studies have estimated centre of mass position for a range of organisms, using various methodologies. However, few studies assess the accuracy of the methods that they employ, and often provide only brief details on their methodologies. As such, no rigorous, detailed comparisons of accuracy and repeatability within and between methods currently exist. This paper therefore seeks to apply three methods common in the literature (suspension, scales and digital modelling) to three 'calibration objects' in the form of bricks, as well as three birds to determine centre of mass position. Application to bricks enables conclusions to be drawn on the absolute accuracy of each method, in addition to comparing these results to assess the relative value of these methodologies. Application to birds provided insights into the logistical challenges of applying these methods to biological specimens. For bricks, we found that, provided appropriate repeats were conducted, the scales method yielded the most accurate predictions of centre of mass (within 1.49 mm), closely followed by digital modelling (within 2.39 mm), with results from suspension being the most distant (within 38.5 mm). Scales and digital methods both also displayed low variability between centre of mass estimates, suggesting they can accurately and consistently predict centre of mass position. Our suspension method resulted not only in high margins of error, but also substantial variability, highlighting problems with this method

Development and Field Testing of the FootFall Planning System for the ATHLETE Robots

The FootFall Planning System is a ground-based planning and decision support system designed to facilitate the control of walking activities for the ATHLETE (All-Terrain Hex-Limbed Extra-Terrestrial Explorer) family of robots. ATHLETE was developed at NASA's Jet Propulsion Laboratory (JPL) and is a large six-legged robot designed to serve multiple roles during manned and unmanned missions to the Moon; its roles include transportation, construction and exploration. Over the four years from 2006 through 2010 the FootFall Planning System was developed and adapted to two generations of the ATHLETE robots and tested at two analog field sites (the Human Robotic Systems Project's Integrated Field Test at Moses Lake, Washington, June 2008, and the Desert Research and Technology Studies (D-RATS), held at Black Point Lava Flow in Arizona, September 2010). Having 42 degrees of kinematic freedom, standing to a maximum height of just over 4 meters, and having a payload capacity of 450 kg in Earth gravity, the current version of the ATHLETE robot is a uniquely complex system. A central challenge to this work was the compliance of the high-DOF (Degree Of Freedom) robot, especially the compliance of the wheels, which affected many aspects of statically-stable walking. This paper will review the history of the development of the FootFall system, sharing design decisions, field test experiences, and the lessons learned concerning compliance and self-awareness

The inertial properties of the German Shepherd

Previously held under moratorium from 30th November 2016 until 30th November 2021The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution.The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution

Instrumentation of a cane to detect and prevent falls

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)The number of falls is growing as the main cause of injuries and deaths in the geriatric community. As a result, the cost of treating the injuries associated with falls is also increasing. Thus, the development of fall-related strategies with the capability of real-time monitoring without user restriction is imperative. Due to their advantages, daily life accessories can be a solution to embed fall-related systems, and canes are no exception. Moreover, gait assessment might be capable of enhancing the capability of cane usage for older cane users. Therefore, reducing, even more, the possibility of possible falls amongst them. Summing up, it is crucial the development of strategies that recognize states of fall, the step before a fall (pre-fall step) and the different cane events continuously throughout a stride. This thesis aims to develop strategies capable of identifying these situations based on a cane system that collects both inertial and force information, the Assistive Smart Cane (ASCane). The strategy regarding the detection of falls consisted of testing the data acquired with the ASCane with three different fixed multi-threshold fall detection algorithms, one dynamic multi-threshold and machine learning methods from the literature. They were tested and modified to account the use of a cane. The best performance resulted in a sensitivity and specificity of 96.90% and 98.98%, respectively. For the detection of the different cane events in controlled and real-life situations, a state-of-the-art finite-state-machine gait event detector was modified to account the use of a cane and benchmarked against a ground truth system. Moreover, a machine learning study was completed involving eight feature selection methods and nine different machine learning classifiers. Results have shown that the accuracy of the classifiers was quite acceptable and presented the best results with 98.32% of overall accuracy for controlled situations and 94.82% in daily-life situations. Regarding pre-fall step detection, the same machine learning approach was accomplished. The models were very accurate (Accuracy = 98.15%) and with the implementation of an online post-processing filter, all the false positive detections were eliminated, and a fall was able to be detected 1.019s before the end of the corresponding pre-fall step and 2.009s before impact.O número de quedas tornou-se uma das principais causas de lesões e mortes na comunidade geriátrica. Como resultado, o custo do tratamento das lesões também aumenta. Portanto, é necessário o desenvolvimento de estratégias relacionadas com quedas e que exibam capacidade de monitorização em tempo real sem colocar restrições ao usuário. Devido às suas vantagens, os acessórios do dia-a-dia podem ser uma solução para incorporar sistemas relacionados com quedas, sendo que as bengalas não são exceção. Além disso, a avaliação da marcha pode ser capaz de aprimorar a capacidade de uso de uma bengala para usuários mais idosos. Desta forma, é crucial o desenvolvimento de estratégias que reconheçam estados de queda, do passo anterior a uma queda e dos diferentes eventos da marcha de uma bengala. Esta dissertação tem como objetivo desenvolver estratégias capazes de identificar as situações anteriormente descritas com base num sistema incorporado numa bengala que coleta informações inerciais e de força, a Assistive Smart Cane (ASCane). A estratégia referente à deteção de quedas consistiu em testar os dados adquiridos através da ASCane com três algoritmos de deteção de quedas (baseados em thresholds fixos), com um algoritmo de thresholds dinâmicos e diferentes classificadores de machine learning encontrados na literatura. Estes métodos foram testados e modificados para dar conta do uso de informação adquirida através de uma bengala. O melhor desempenho alcançado em termos de sensibilidade e especificidade foi de 96,90% e 98,98%, respetivamente. Relativamente à deteção dos diferentes eventos da ASCane em situações controladas e da vida real, um detetor de eventos da marcha foi e comparado com um sistema de ground truth. Além disso, foi também realizado um estudo de machine learning envolvendo oito métodos de seleção de features e nove classificadores diferentes de machine learning. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e apresentou, como melhores resultados, 98,32% de precisão para situações controladas e 94.82% para situações do dia-a-dia. No que concerne à deteção de passos pré-queda, a mesma abordagem de machine learning foi realizada. Os modelos foram precisos (precisão = 98,15%) e com a implementação de um filtro de pós-processamento, todas as deteções de falsos positivos foram eliminadas e uma queda foi passível de ser detetada 1,019s antes do final do respetivo passo de pré-queda e 2.009s antes do impacto