Calibration of smartphone light sensors with a near field communication enabled reference

Leikanger, Tore; Schuss, Christian; Häkkinen, Juha

doi:10.1109/icsens.2016.7808930

Cited by 4 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Activities such as walking, walking on stairs, jogging, jumping, driving a motorcycle, driving a car or holding a mobile in hand and placing the mobile on table are known as simple activities. Commonly used sensors in mobile phones are accelerometer [25,26], gyroscope [27], magnetometer [28,29], proximity sensor, light sensor [30], barometer [31,32], thermometer, air humidity sensor [33,34], etc. The user activity can be recognized with the help of these sensors.…”

Section: Activity-recognition Algorithm (Algorithm 3)mentioning

confidence: 99%

Active Volume Control in Smart Phones Based on User Activity and Ambient Noise

Kumar

Malathi

Kumar

et al. 2020

Sensors

View full text Add to dashboard Cite

To communicate efficiently with a prospective user, auditory interfaces are employed in mobile communication devices. Diverse sounds in different volumes are used to alert the user in various devices such as mobile phones, modern laptops and domestic appliances. These alert noises behave erroneously in dynamic noise environments, leading to major annoyances to the user. In noisy environments, as sounds can be played quietly, this leads to the improper masked rendering of the necessary information. To overcome these issues, a multi-model sensing technique is developed as a smartphone application to achieve automatic volume control in a smart phone. Based on the ambient environment, the volume is automatically controlled such that it is maintained at an appropriate level for the user. By identifying the average noise level of the ambient environment from dynamic microphone and together with the activity recognition data obtained from the inertial sensors, the automatic volume control is achieved. Experiments are conducted with five different mobile devices at various noise-level environments and different user activity states. Results demonstrate the effectiveness of the proposed application for active volume control in dynamic environments.

show abstract

Section: Activity-recognition Algorithm (Algorithm 3)mentioning

confidence: 99%

Active Volume Control in Smart Phones Based on User Activity and Ambient Noise

Kumar

Malathi

Kumar

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In previous studies, we have analysed the performance of ambient light sensors (ALSs) in smartphones [9]- [11] in addition to presenting the opportunity to enable external sensors for smartphones [4], [12]. The focus lied on sensors which are not available on smartphones on the one hand [4] and the calibration of existing sensors in smartphones on the other [12]. The target was to enable external sensors which can be connected to the inter-integrated circuit (I2C) bus.…”

Section: A Research Backgroundmentioning

confidence: 99%

Efficient external sensors for smartphones through near field communication (NFC)

Schuss

Leikanger

Häkkinen

2018

2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

Self Cite

View full text Add to dashboard Cite

This paper evaluates the opportunity to utilise external sensors through near field communication (NFC) for smartphones, both, in terms of energy consumption from the smartphones' battery and time consumption of the smartphones' user. Our developed sensor card enables external sensors to obtain information, for example, on the current temperature level, humidity level, UV level, and amount of ambient light. We demonstrate that our approach requires significantly less energy and time compared to alternatives which users of smartphones have. Furthermore, we analyse the impact of heat sources present in smartphones on the readings of temperature and humidity level sensors. We propose to externalise sensors in order to minimise the negative impact of temperature on sensors readings and, thereby, improving the accuracy of the measurements and the acquired sensor data.

show abstract