Accuracy of 2 Activity Monitors in Detecting Steps in People With Stroke and Traumatic Brain Injury

Fulk, George; Combs, Stephanie A.; Danks, Kelly A.; Nirider, Coby D.; Raja, Bhavana; Reisman, Darcy S.

doi:10.2522/ptj.20120525

Cited by 169 publications

(133 citation statements)

References 45 publications

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“…The major downside of these devices is the limited set of features they provide: number of steps taken, distance walked, energy expenditure (kcal) and sleep duration. Moreover, the quality of information they provide is usually not satisfactory, as revealed for example by the following studies: Fitbit Ultra 5 is found to have fair to good accuracy in identifying steeping activity for stroke and TBI patients [42] with an Intraclass Correlation Coe cient (ICC) of 0.73, and for healthy subjects agreement was above 95% [68]. The same study found Nike+ Fuelband 6 to have an ICC of 0.2.…”

Section: Wearable Sensor-based Armentioning

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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Section: Wearable Sensor-based Armentioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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“…Furthermore, during the performance of a circuit of activities of daily living, the device significantly underestimated activity time but provided an acceptable estimation of EE in a group basis. 36 The StepWatch Activity Monitor (SAM) (Orthocare Innovations, Seattle, WA) is an accelerometer worn on the ankle that measures step counts from all walking. The step counts from SAM, validated against manual counts, proved to have an accuracy >90% in 99% of 127 COPD patients.…”

Section: New Motion Sensors Used For Pa Measurement In Copdmentioning

confidence: 99%

Physical Activity Monitoring in Patients with Chronic Obstructive Pulmonary Disease

Liao¹,

Benzo²,

Ries³

et al. 2014

J COPD F

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Reduced physical activity (PA) in patients with chronic obstructive pulmonary disease (COPD) is associated with increased morbidity and mortality (e.g. exacerbations) and eventually leads to disability, depression, and social and physical isolation. Measuring PA in this population is important to accurately characterize COPD and to help clinicians during a baseline evaluation and patient follow-up. Also, it may help increase adherence to PA programs. There are reliable objective and subjective methods available to measure PA. Recently, several new monitors have been developed that have improved accuracy of such measurements. Because these devices provide real-time feedback, they may help to improve participant self-motivation strategies and reinforce daily lifestyle modifications, one of the main goals in COPD management. This review focuses on describing available instruments to measure PA, specifically in patients with COPD. The reliability, validity, advantages, limitations, and clinical applications of questionnaires, pedometers, and accelerometers are discussed. Finally, based on current published literature, we propose recommendations about which methods may be most useful in different research or clinical settings.

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“…We need to consider the security measure that how we can utilize the personal data without causing any security bleach [4]. In this architecture, we have four stages and in the first stage, we have a sensor which will trap the all the data from the surrounding environment, in the second stage, we have a middleware like a data converter which will convert the data captured by the sensor into human readable language which will be having the combination of IoT and ML.…”

Section: Introductionmentioning

confidence: 99%

Multiple Architectural Approach for Urban Development Using Wearable IoT Devices: A Combined Machine Learning Approach

Mylavarapu

2018

AIT

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Machine Learning becomes a part of our life in recent days and everything we do in interlinked with machine learning. As a technocrat, we tried to implement machine learning with Internet of Things (IoT) for better implementation of technology in organizations for security. We designed an sample architecture which will carry the burden of safeguarding the organizational data with IoT using machine learning with an effective manner and in this case we were proposing utilization of cloud computing for better understanding of data storage and retrieval process. Machine learning is used for the prediction models based on which we need to perform high level analysis of data and using IoT we promote authorization mechanism based on which we recognize the appropriate recipient of data and cloud for managing the data services with the three-tier architecture. We present the architecture we are proposing for better utilization of machine learning and IoT with cloud architecture.

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Accuracy of 2 Activity Monitors in Detecting Steps in People With Stroke and Traumatic Brain Injury

Abstract: The Fitbit Ultra may be a low-cost alternative to measure the stepping activity in level, predictable environments of people with stroke and TBI who can walk at speeds ≥0.58 m/s.

Cited by 169 publications

References 45 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

Physical Activity Monitoring in Patients with Chronic Obstructive Pulmonary Disease

Multiple Architectural Approach for Urban Development Using Wearable IoT Devices: A Combined Machine Learning Approach

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