Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases

Billiet, Lieven; Swinnen, Thijs Willem; Westhovens, René; Vlam, Kurt de; Huffel, Sabine Van

doi:10.3390/s16122151

“…Nevertheless, patterns also contain inherent noise in the sense that parts of the movement are not defining aspects of a particular activity. Current results are promising for many subjects, but an earlier study on the Sensewear dataset showed that much is to be gained by combining pattern features with more general statistical features [47]. In the future, it could be beneficial to merge the feature set obtained by HODA with the statistical features employed there.…”

Section: Discussionmentioning

confidence: 99%

“…The dynamic regions of the signal are subsequently separated from the more static ones. This separation was implemented as a step-wise adaptive approach [47].…”

Section: Segment Identificationmentioning

confidence: 99%

Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach

Billiet

¹

,

Swinnen

²

,

Vlam

³

et al. 2018

Self Cite

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Abstract:In current clinical practice, functional limitations due to chronic musculoskeletal diseases are still being assessed subjectively, e.g., using questionnaires and function scores. Performance-based methods, on the other hand, offer objective insights. Hence, they recently attracted more interest as an additional source of information. This work offers a step towards the shift to performance-based methods by recognizing standardized activities from continuous readings using a single accelerometer mounted on a patient's arm. The proposed procedure consists of two steps. Firstly, activities are segmented, including rejection of non-informative segments. Secondly, the segments are associated to predefined activities using a multiway pattern matching approach based on higher order discriminant analysis (HODA). The two steps are combined into a multi-layered framework. Experiments on data recorded from 39 patients with spondyloarthritis show results with a classification accuracy of 94.34% when perfect segmentation is assumed. Automatic segmentation has 89.32% overlap with this ideal scenario. However, combining both drops performance to 62.34% due to several badly-recognized subjects. Still, these results are shown to significantly outperform a more traditional pattern matching approach. Overall, the work indicates promising viability of the technique to automate recognition and, through future work, assessment, of functional capacity.

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“…The vest provides a portable and low-cost solution that can be used indoors and outdoors to provide long-term care at home. The recognition abilities of accelerometerbased detection and activity monitoring can help in assessing rheumatic and musculoskeletal diseases [7]. There are several other examples of similar research and development studies that are shifting the paradigm of patient care and welfare.…”

Section: Background Workmentioning

confidence: 99%

Application of Evaluation Parameters for Successful Transmission in wireless sensor networks

Rannels¹,

Falconieri²,

Phillips³

et al. 2018

IJRTER

0

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Abstract:In this paper, we have presented a novel transmission protocol which is suited for battery-powered sensors that are worn by a patient when under medical treatment, and allow constant monitoring of health indices. These body-wearable sensors log data from the patient and transmit the data to a base-station or gateway, via a wireless link at specific intervals. The signal link quality varies because the distance between the patient and the gateway is not fixed. This may lead to packet drops that increase the energy consumption due to repeated retransmission. The proposed novel transmission power control protocol combines a state based adaptive power control (SAPC) algorithm and an intelligent adaptive drop-off algorithm, to track the changes in the link quality, in order to maintain an acceptable Packet success rate (PSR)(~99%). This removes the limitation of the SAPC by making the drop-off rate adaptive. Simulations were conducted to emulate a subject's movement in different physical scenarios-an indoor office environment and an outdoor running track. The simulation results were validated through experiments in which the transmitter, together with the sensor mounted on the subject, and the subject themselves were made to move freely within the communicable range. Results showed that the proposed protocol performs at par with the best performing SAPC corresponding to a fixed drop-off rate value.

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“…The vest provides a portable and low-cost solution that can be used indoors and outdoors to provide long-term care at home. The recognition abilities of accelerometer-based detection and activity monitoring can help in assessing rheumatic and musculoskeletal diseases [7]. There are several other examples of similar research and development studies that are shifting the paradigm of patient care and welfare.…”

Section: Background Workmentioning

confidence: 99%

Novel Adaptive Transmission Protocol for Mobile Sensors that Improves Energy Efficiency and Removes the Limitation of State Based Adaptive Power Control Protocol (SAPC)

Basu

¹

,

Gupta

²

,

Moretti

³

et al. 2017

JSAN

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Abstract:In this paper, we have presented a novel transmission protocol which is suited for battery-powered sensors that are worn by a patient when under medical treatment, and allow constant monitoring of health indices. These body-wearable sensors log data from the patient and transmit the data to a base-station or gateway, via a wireless link at specific intervals. The signal link quality varies because the distance between the patient and the gateway is not fixed. This may lead to packet drops that increase the energy consumption due to repeated retransmission. The proposed novel transmission power control protocol combines a state based adaptive power control (SAPC) algorithm and an intelligent adaptive drop-off algorithm, to track the changes in the link quality, in order to maintain an acceptable Packet success rate (PSR)(~99%). This removes the limitation of the SAPC by making the drop-off rate adaptive. Simulations were conducted to emulate a subject's movement in different physical scenarios-an indoor office environment and an outdoor running track. The simulation results were validated through experiments in which the transmitter, together with the sensor mounted on the subject, and the subject themselves were made to move freely within the communicable range. Results showed that the proposed protocol performs at par with the best performing SAPC corresponding to a fixed drop-off rate value.Keywords: adaptive transmission; energy efficiency; mobile sensors Background WorkThe emergence of Internet of Thing (IoT) has enabled technological capabilities to exchange data and to create a healthcare system that is efficient in terms of time, energy and cost [1]. In healthcare, body wearable sensors are used to continuously monitor the vital physiological parameters of patients in hospitals and the elderly at home, allowing them to enjoy independent living [2]. Hospitals use IoT to monitor the location of medical devices, personnel and patients. The healthcare professionals are then able to use data to create a system of proactive management with this network of devices. At the same time, such procedures are effective and are cost-effective ways of monitoring age-related illnesses [3]. For example, one Texas hospital reportedly cut the re-admission rate for patients with heart failures by 50% using predictive analysis of their individual healthcare records [4].There have been significant developments in building body wearable sensors that have low computational complexity, require little memory storage, and can be suitably implemented using simple hardware. For example, Shih-Hong Li et al. have designed a wearable sensor to detect real time wheezing [5]. In the field of medicine, the wheezing sound is usually considered as an indicator of the degree of airway obstruction. In [6], authors have developed a wearable instrumented vest

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Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases

Cited by 11 publications

References 42 publications

Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach

Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach

Application of Evaluation Parameters for Successful Transmission in wireless sensor networks

Novel Adaptive Transmission Protocol for Mobile Sensors that Improves Energy Efficiency and Removes the Limitation of State Based Adaptive Power Control Protocol (SAPC)

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