Discrimination of walking patterns using wavelet-based fractal analysis

Shimizu, Masaki; Tamura, Toshiyo; Akay, Metin; Fujimoto, Toshiro; Togawa, Tatsuo; Fukui, Yasuhiro

doi:10.1109/tnsre.2002.802879

Cited by 197 publications

(134 citation statements)

References 29 publications

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“…In [52,53,54] the authors use the discrete wavelet transform to classify the acceleration signal for horizontal level and stairway walking. Other authors [1] have used the Daubechies wavelets over the preprocessed signals with the SVM transformation.…”

Section: Wavelet Analysismentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

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Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

505

350

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The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care, and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers it is possible to determine if a user is walking or running at a specific time of day. By correlating this knowledge with GPS data it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

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Section: Wavelet Analysismentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

Figo

Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

505

350

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“…This article uses a range of ± 8g. Data reception and storage mainly by the phone, through the APP in the phone to complete [4] . The communication between the two parts of the data acquisition module is done via the Bluetooth module.…”

Section: Data Collectionmentioning

confidence: 99%

Recognition Algorithm of Activity Recognition Based on Single Accelerometer

Yan¹,

Li²

2017

2017 6th International Conference on Advanced Materials and Computer Science (ICAMCS 2017)

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Abstract. In order to improve the accuracy and variety of human motion recognition in the acceleration signal recognition. In this experiment, in addition to the station, lying, sitting, walking, running, walking speed, upstairs, downstairs action recognition of traditional research, this paper also adds to the left turn right 180 degrees, 180 degrees, 90 degrees, turn left, turn right 90 degrees, from lying to sitting, sitting from the station, from the station to sit down. Sixteen kinds of action from sit to, and used to monitor human motion more accurately. In this paper, a new SVM-KNN hierarchical classification model is proposed to identify the sixteen kinds of human actions. The experimental results show that the recognition algorithm has a high recognition rate.

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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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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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Discrimination of walking patterns using wavelet-based fractal analysis

Cited by 197 publications

References 29 publications

Preprocessing techniques for context recognition from accelerometer data

Preprocessing techniques for context recognition from accelerometer data

Recognition Algorithm of Activity Recognition Based on Single Accelerometer

Classification and suitability of sensing technologies for activity recognition

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