The Jigsaw continuous sensing engine for mobile phone applications

Lu, Hong; Yang, Jun; Liu, Zhigang; Lane, Nicholas D.; Choudhury, Tanzeem; Campbell, Andrew T.

doi:10.1145/1869983.1869992

Cited by 461 publications

(370 citation statements)

References 17 publications

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“…Then similar recurring activities are grouped together to find routine behaviors. Specifically, activity states such as walking, running, stationary and in-vehicle are automatically tracked using movement sensors within the phone, these activities are also tagged with the geographical location [40]. Other physical activities that cannot be automatically inferred by phone sensors can be manually input using a drop-down menu that contains a searchable pre-loaded set [41].…”

Section: Routine Behavior Recognition Modulementioning

confidence: 99%

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults (Preprint)

Rabbi¹,

Aung²,

Gay³

et al. 2018

Preprint

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Chronic Pain (CP) is defined as pain that persists despite the resolution of injury or pathology [1] and is attributed to changes in the central and peripheral nervous system resulting in amplified or uninhibited pain signals [2,3]. The persistency of pain signals over long periods of time can severely affect people both physically and psychologically.CP is closely linked with distress, can affect behavior, lead to a loss of productivity and daily functioning. Among people with CP, 86% report an inability to sleep well [4], 70% report trouble concentrating [4], 44-51% report anxiety [5], 88% express anger due to not seeing improvement [5]; CP related productivity related losses are 4.6 hour per week (5.5 hours/ week for chronic back pain) [6]. These adverse effects of CP can further result in depression and mood disorders; 40-50% CP population suffer from depressive disorders, while 77% report feeling depressed [5,7,8,9]. In terms of prevalence, the World Health Organization recognizes CP as a public health problem worldwide [10] (32% in low income countries and 30% in high income countries) [11,12]. In the US alone, 120 million adults are reported to be suffering from CP [13].Related costs have exceeded diabetes, cancer and heart disease in US [14] and CP is also related to substance abuse; 12 million (12 year or older) reported non-medical use of pain medication and overdose related deaths are rising every year [15].A particularly common form of CP is musculoskeletal chronic pain, which affects one in ten adults globally; 28.1% of the CP population has a musculoskeletal form.Musculoskeletal CP is a leading cause of disability, with 28% of people with chronic back pain reporting limited movement due to the condition [16]. Having said this, it is well understood that successful management in musculoskeletal CP is achievable with regular and sustained physical activity [10]. This is due to regular activity having the effect of protecting against muscle weakening and also inhibiting the neurophysiological mechanisms underlying the spread of pain [4]. Physical activity also has fewer side effects than drugs [17]. The National Institute of Health Care in the UK (NICE) recommends reducing the dependence on drugs and suggests exercise to be a core treatment for CP [10]. Despite these benefits, adherence to regular and sustained physical activity is very low [12] and the need to improve adherence to prescribed physiotherapies among individuals with musculoskeletal CP has been a longstanding problem [18]. This is especially challenging in self-managed settings where there is no care provider present to offer encouragement and respond to the individual's specific needs in real time [12]. This low adherence is principally due to the perceived onset of pain exacerbation due to physical activity which over time results in psychological barriers. Some barriers manifest at an affective level [5], typically fear and anxiety [19,20] which are also closely linked to more cognitive barriers such as negative beliefs [5] and catast...

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Section: Routine Behavior Recognition Modulementioning

confidence: 99%

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults (Preprint)

Rabbi¹,

Aung²,

Gay³

et al. 2018

Preprint

Self Cite

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“…Other examples of using mobile phones for individual-based social sensing are Montoliu et al [12], Lu et al [13], and projects coming from CENS center, e.g. Campaignr by Joki et al [14], as well as additional works described in [15].…”

Section: Individual Based Data Collectionmentioning

confidence: 99%

How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage

Altshuler

Fire

Aharony

et al. 2012

Social Computing, Behavioral - Cultural Modeling and Prediction

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“…For example, Kozina's system [14] sends calls directly to voicemail if a user is jogging and generates a daily/weekly activity profile to determine if a user is performing a healthy amount of exercise. Lu [15] implemented two simple proof-of-concept applications using a continuous sensing engine, JigMe, that automatically records a user's daily diary and, GreenSaw, that gives users awareness of their daily calorie expenditure and carbon footprint. These add-ons increase the value of the research and evidence that they could be used out of the laboratory by common users.…”

Section: Introductionmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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The Jigsaw continuous sensing engine for mobile phone applications

Cited by 461 publications

References 17 publications

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults (Preprint)

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults (Preprint)

How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

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