Classification of waist-acceleration signals in a continuous walking record

Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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“…A T [37], [110], [100], [85], [74], [56], [130], [104], [2], [45], [98], [103], [60], [47], [131], [119], [31], [70] Sitting(0)…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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“…In addition, instead of decomposing the kinematic signal to sinusoids, wavelet transform uses a suitable basic function, which is more similar to the typical patterns in kinematic data. Several investigators have shown the advantages of wavelet transform for the analysis of kinematic signals (Sekine et al 2000;Lakany 2000;Paraschiv-Ionescu et al 2004;NajaW et al 2002).…”

Section: Main Types Of Body Wxed Sensorsmentioning

confidence: 99%

Mobility assessment in older people: new possibilities and challenges

2007

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A major challenge for researchers and clinicians who address health issues in the ageing population is to monitor functioning, and to timely initiate interventions that aim to prevent loss of functional abilities and to improve the quality of life of older people. With the progress of technologies in the last decades, methods have become available that use body Wxed sensors (BFS) to measure aspects of human performance under real-life conditions. These methods are based on the use of miniaturised and integrated sensors in combination with lightweight, small measuring devices that both can be carried on the body without interfering with normal behaviour. This paper addresses the potential relevance of new technology for monitoring motor function in older people, thereby speciWcally focusing on mobility assessment. After a short introduction with background information about BFS based technology, this paper identiWes areas of particular relevance, and discusses the application of ambulatory techniques for long-term monitoring of daily physical activity, fall detectors, fall risk evaluation, and assessment of motor performance such as gait and balance control. Examples are given how these techniques can become clinically relevant, particularly in the context of fall interventions for older people.

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“…Por ejemplo, para la medida del movimiento de la pierna durante la marcha se coloca un acelerómetro en el muslo o tobillo en el segmento corporal que se quiere estudiar [33][34][35] . También existen estudios cuyo objeto hace referencia al cuerpo humano completo y, en ese caso se coloca un acelerómetro triaxial cerca del centro de masas, en la pelvis, consiguiendo así información muy útil e interesante [36][37][38] . Éste es el modelo que se sigue en el estudio que se ha realizado.…”

Section: Acelerómetros Y Giróscopos Aplicados a La Monitorización Delunclassified

“…La aceleración aumenta en magnitud de la cabeza a los tobillos. Correr produce las aceleraciones mayores en la dirección vertical de 8,1 -12 g en el tobillo [35][36][37][38][39][40][41][42][43][44] , hasta 5,0g en la parte lumbar [35][36][37][38][39][40][41][42][43][44] y hasta 4,0 en la cabeza 44 . Las aceleraciones del tronco en el eje vertical que se han obtenido durante la marcha abarcan el rango -0,3 a 0,8 g, mientras que en dirección horizontal medido en la zona lumbar el rango va de -0,3 a 0,4 g y de -0,2 a 0,2 en la cabeza 45 .…”

Section: Acelerómetros Y Giróscopos Aplicados a La Monitorización Delunclassified

“…Diversos estudios con acelerómetros han examinado las actividades de levantarse y sentarse. Estas actividades pueden ser incluidas como periodos de actividad 42 y pueden ser clasificadas e identificadas por reconocimiento de las posturas previas y posteriores como estar sentado y levantado [33][34][35][36][37][38][39][40] . Un estudio preliminar encontró una correlación [r=0,537] entre las aceleraciones medidas mediante un aceleró-metro colocado en la cintura durante la actividad de levantarse y sentarse y el riesgo de caída en 37 sujetos mayores 61 .…”

Section: Movimiento De Sentarse Y Levantarseunclassified

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Valoración de la capacidad funcional en el ámbito domiciliario y en la clínica: Nuevas posibilidades de aplicación de la acelerometría para la valoración de la marcha, equilibrio y potencia muscular en personas mayores

Izquierdo

Martínez-Ramírez

Larrión

et al. 2008

Anales Sis San Navarra

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REVISIONESValoración de la capacidad funcional en el ámbito domiciliario y en la clínica. Nuevas posibilidades de aplicación de la acelerometría para la valoración de la marcha, equilibrio y potencia muscular en personas mayores Functional capacity evaluation in a clinical and ambulatory setting: new challenges of accelerometry to assessment balance and muscle power in aging population RESUMENDentro de cualquier población de individuos mayores de 65 años, una proporción sustancial (entre el 6% y el 25%) sufre diferentes síntomas del síndrome de fragilidad. A pesar de la complejidad del termino fragilidad y de las imprecisiones en cuanto a su definición existe un consenso sobre sus síntomas y signos. Las personas que poseen este síndrome presentan pérdidas de fuerza muscular, fatiga, disminución de la actividad física, con un aumento del riesgo de padecer anorexia-pérdida de peso, delirium, hospitalización, declive funcional, deterioro cognitivo, mortalidad, ingreso en residencias, caídas e inestabilidad.

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Classification of waist-acceleration signals in a continuous walking record

Cited by 89 publications

References 6 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

Mobility assessment in older people: new possibilities and challenges

Valoración de la capacidad funcional en el ámbito domiciliario y en la clínica: Nuevas posibilidades de aplicación de la acelerometría para la valoración de la marcha, equilibrio y potencia muscular en personas mayores

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