A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity

Curone, Davide; Bertolotti, Gian Mario; Cristiani, Andrea; Secco, Emanuele Lindo; Magenes, Giovanni

doi:10.1109/titb.2010.2050696

Cited by 91 publications

(59 citation statements)

References 24 publications

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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%

“…A T [74], [100], [110], [62], [130], [2], [45], [60], [47], [131], [31], [70] Kin [8], [90]; Cam [29], [54], [75], [41]; grey Cam [49]; Cam+ 2xA T [111] Lying(0)…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

“…A T [37], [100], [62], [74], [56], [130], [2], [45], [60], [47], [31] Kin [90]; Cam [29], [54], [75], [41]; grey Cam [49] Evidence found in the literature suggest that for the recognition of all the activities presented in the above table, apart from Using Elevator, Walk Through Doors and Leaving the House, a single tri-axial accelerometer is sufficient. The main advantage of such wearable technology is the fact that it is widely available and relatively cheap.…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

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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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“…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%

“…A T [74], [100], [110], [62], [130], [2], [45], [60], [47], [131], [31], [70] Kin [8], [90]; Cam [29], [54], [75], [41]; grey Cam [49]; Cam+ 2xA T [111] Lying(0)…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

Section: Locomotion Transitions and Posturementioning

confidence: 99%

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Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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“…Their advantages are cost efficiency, light weight, small size, low power consumption, and convenience in the independent living environment (3) . Many researchers have developed algorithms to distinguish daily-life activities from falling events.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

Prapochanang

Leauhatong

Nattaitanakul

2016

The Proceedings of the 4th International Conference on Industrial Application Engineering 2016

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This paper proposes a novel dimension reduction technique for physical activity recognition from a waistmounted accelerometer. Firstly, the wavelet transform is used to extract features from the acceleration signals. Then the proposed technique is used to reduce the dimension of the wavelet features. Finally, the Multi-Layer Perceptron Neural Network (MLPNN) is used to recognize the physical activities from the reduced features. In our experiments, 5 volunteers who were healthy with the ages between 21 to 25 year old were asked to mount a tri-axial accelerometer at the right side of their waists. Next, the volunteers were asked to perform 5 daily-life physical activities: 1) walking 2) standing up from a chair 3) sitting down on a chair 4) lying down on a bed and 5) getting up from a bed; and 5 falling events: 1) forward fall 2) backward fall 3) falling to the right side 4) falling to the left side and 5) falling when standing up. In order to evaluate the performance of the proposed algorithm, the precision of the recognition and the total number of the used operators were used. The performance of the recognition with different setting of mother wavelets, vanishing moments, the rate of the dimension reduction, and the number of nodes of the hidden layer were evaluated. From the experiments, the proposed dimension reduction not only reduce the total number of the used operators but also increase the precision of the recognition.

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“…Their recent "disruptive impact" is mainly due to their small size, low-invasiveness and intuitiveness, relatively low cost, and easy integration with existing platforms for sensor networks. They are also able to effectively provide indicative features of human movement corresponding to frequency and intensity of motion, which is important to assess, for instance, static posture characteristics [9].…”

Section: Introductionmentioning

confidence: 99%

HuMOVE: A Low-invasive Wearable Monitoring Platform in Sexual Medicine

Ciuti

Nardi

Valdastri

et al. 2014

Urology

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Number of tables: 1Keywords: Accelerometer-based platform; activities of daily living; human sexual performance evaluator; sexual assessment monitor; wearable inertial device; premature and delayed ejaculation. Methods: Design, implementation and development of HU-MOVE, a wearable platform equipped with an accelerometer sensor for monitoring inertial parameters for sexual performance assessment and diagnosis. The system enables quantitative measurement of movement parameters during sexual intercourse, meeting the requirements of wearability, data storage, sampling rate and interfacing methods, which are fundamental for human sexual intercourse performance analysis. HU-MOVE was validated through characterization on a controlled experimental test bench and tested in human model in simulated sexual intercourse conditions.Results: HU-MOVE demonstrated to be a robust and quantitative monitoring platform and a reliable candidate for sexual performance evaluation and diagnosis. Characterization analysis on the controlled experimental test bench demonstrated an accurate correlation between the HU-MOVE system and a reference displacement sensor data. Experimental test in human model in simulated intercourse condition confirmed the accuracy of the sexual performance evaluation platform and the effectiveness of the selected and derived parameters. The obtained outcomes also established the project expectations in terms of usability and comfort, highlighted by the questionnaires that pointed out the low-invasiveness and acceptance of the device. Conclusions:To the best of our knowledge, HU-MOVE platform is the first device for human sexual performance analysis compatible with the sexual intercourse; the system has 4 the potentiality to represent a helpful tool for physicians to accurately classify sexual disorders, e.g. premature or delayed ejaculation. IntroductionOver the past few decades the increased level of awareness within health and physical fitness communities has created an emerging need for smart applications and comfortable/wearable devices that sense, classify, and provide feedback to users' activities of daily living in a pervasive fashion [1][2][3][4][5][6]. In particular, physical motion, which is a fundamental aspect for categorizing person's current health and activity, can be detected and recorded by using wearable accelerometer-based systems [7,8]. Their recent "disruptive impact" is mainly due to their small size, low-invasiveness and intuitiveness, relatively low cost, and easy integration with existing platforms for sensor networks. They are also able to effectively provide indicative features of human movement corresponding to frequency and intensity of motion, which is important to assess, for instance, static posture characteristicsAn important field that may benefit from the use of a wearable monitoring inertial device is the sexual medicine, due to its consistent impact and influence on human life quality and HU-MOVE is intended for SI use by a male human operator and ensures compatibility and mi...

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A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity

Cited by 91 publications

References 24 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

HuMOVE: A Low-invasive Wearable Monitoring Platform in Sexual Medicine

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