2018
DOI: 10.1007/s11042-018-5867-y
|View full text |Cite
|
Sign up to set email alerts
|

Wearable data analysis, visualisation and recommendations on the go using android middleware

Abstract: Wearable technology comes with the promise of improving one's lifestyles thru data mining of their physiological condition. The potential to generate a change in daily or routine habits thru these devices leaves little doubt. Whilst the hardware capabilities of wearables have evolved rapidly, software apps that interpret and present the physiological data and make recommendations in a simple, clear and meaningful way have not followed a similar pattern of evolution. Existing fitness apps provide routinely some… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
8
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 20 publications
(21 citation statements)
references
References 21 publications
0
8
0
Order By: Relevance
“…All included studies used accelerometer sensors. We could categorize these devices into three groups: (1) commercial wrist-worn wearable accelerometers that are consumer-grade devices with a sample rate between 30 Hz and 60 Hz, such as Fitbits [ 13 , 14 , 19 , 22 , 25 ], Samsung Gear [ 17 ], and Nokia [ 15 ]; (2) smartphone accelerometers with a sample rate usually set to 50 Hz and up to 100 Hz, in which data were collected via an app installed in the smartphone [ 7 , 9 , 10 , 16 , 18 ]; and (3) scientifically validated wearable accelerometers with a sample rate up to 100 Hz, such as ActiGraph [ 8 ], GENEActiv [ 11 , 24 ], and other devices developed for health care [ 12 , 20 ].…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…All included studies used accelerometer sensors. We could categorize these devices into three groups: (1) commercial wrist-worn wearable accelerometers that are consumer-grade devices with a sample rate between 30 Hz and 60 Hz, such as Fitbits [ 13 , 14 , 19 , 22 , 25 ], Samsung Gear [ 17 ], and Nokia [ 15 ]; (2) smartphone accelerometers with a sample rate usually set to 50 Hz and up to 100 Hz, in which data were collected via an app installed in the smartphone [ 7 , 9 , 10 , 16 , 18 ]; and (3) scientifically validated wearable accelerometers with a sample rate up to 100 Hz, such as ActiGraph [ 8 ], GENEActiv [ 11 , 24 ], and other devices developed for health care [ 12 , 20 ].…”
Section: Resultsmentioning
confidence: 99%
“…In 7 out of the 19 (37%) selected studies, accelerometers were used with other sensors such as GPS tracking [ 10 , 16 , 17 ], compass position tracking [ 17 , 20 ], heart rate trackers [ 17 , 21 ], and smart scales [ 15 , 19 ].…”
Section: Resultsmentioning
confidence: 99%
“…The analytical usage of this data, as it has been discussed throughout this current review article, depends on approaches to access and distribute the data. The development of wearables-associated software directed at both accurate health monitoring and state of the art data collection, analysis and visualization is of the utmost importance [ 89 ]. Both standalone and hybrid methods for anomaly detection require accurate interpretations that can be correlated with the user activity and their daily experience.…”
Section: Prospectsmentioning
confidence: 99%
“…Another important area is the transparency of algorithms designed to compute steps or sleep data, which should be encouraged. The limitations of the large-scale data generated from wearables need to be addressed more often [ 89 ].…”
Section: Prospectsmentioning
confidence: 99%
“…[ 12 ] generated daily personalised text messages with custom timing, frequency, and feedback about their step count/goal and with motivational content to support reflection using a multiarmed bandit (MAB) algorithm and the number of minutes spent in PA. Ref. [ 13 ] used a genetic algorithm, pareto-optimality, and the participants’ daily sleep duration, steps, calories, exercise duration, exercise distance, exercise calories, step count, step distance, and step calories to analyse the wearer’s data and make personal lifestyle improvement recommendations. Ref.…”
Section: Related Workmentioning
confidence: 99%