Table 2.

Smartphone-sensing capabilities and application scenarios.

Sensor (S)/ Software Feature (SF)	What Does It Collect?	What Has It Been Used for?	Key Advantages (+)/Disadvantages (−)
Accelerometer (S)	Acceleration forces along x, y, and z axes of the device	It has been used to detect physical activity (such as standing, walking, running, etc.) and sedentary behavior [11,23,53]. Physical activity has also been used to infer mental wellbeing of individuals [15,67,92] (e.g., decline in physical activity impacting mental health)	+ Relatively privacy-sensitive. + Low power − Accuracy impacted by sampling rate. − Performance negatively impacted by device placement.
Ambient Light (S)	Amount of light the device is exposed to	It has been used alongside other sensors to understand the user surroundings. Studies used the data to infer when the user was asleep [13,57,58] and infer the amount of spent in the dark, which could provide an indication of mood/mental health [15,26,80]	− Only able to make very limited inferences by itself, used in conjunction with other sensors − Potentially impacted by device placement
Application usage (SF)	Information about the applications used on the device	It has been used to infer the communication behavior of users. Information such as application use time and genres of applications (e.g., social media) used provided an insight into the user’s sociability and wellbeing [55,70,92].	+ Can be used to infer a wide range of user interactions − Privacy concerns depending on what information is captured.
Battery status (SF)	Indicates the phone charging status (on/off)	It was used as a proxy measure to infer phone-usage behavior. For example, studies monitoring sleep used it as an indicator of the person sleeping, assuming they charge their phone overnight [19,57].	+ Privacy-sensitive − Only able to make limited inferences by itself, used in conjunction with other sensors
Bluetooth (S)	Information about nearby Bluetooth-enabled devices	It has been used to infer the sociability of the user. By collecting information such as count of nearby Bluetooth devices, number of recurring devices etc., studies were able to infer the social context of users [9,61,76].	− Not all nearby devices may have Bluetooth turned on
Camera (S)	Capture images and videos	It has been used to infer the user’s emotions by capturing facial images [71]. Another study used the camera to capture eye-movement data and checked if such features could provide an indication of the user emotions [74].	+ Ability to visually monitor user behavior − Higher impact on battery life − Relatively serious privacy concerns, due to video recording.
Global Positioning System (GPS) (S)	Latitudinal and longitudinal coordinates indicating physical location	It has been used to infer the mobility of a user (number of places visited, time spent outdoors, time spent at home) which has an impact on wellbeing [26,27,84] (e.g., too much time spent at home indicating a decline in sociability and in turn mental health [7])	+ Can use location to make a wide range of inferences about behavior and wellbeing. − Higher impact on battery life compared to other modes of sensing. − Privacy concerns, especially when used with a high degree of granularity.
Gyroscope (S)	Rotational forces along the x, y, and z axes of the device	It has been used in conjunction with the accelerometer for activity recognition. Assisted in detecting activities such as walking, standing, laying etc. [11,30,49]	+ Can increase recognition accuracy compared to an accelerometer alone, due to the provision of additional rotational information. + Low power − Impacted by device placement
Microphone (S)	Collect audio recordings from the surroundings	It has been used to infer surrounding sound, which can provide information about the user’s context. Some studies used it to detect if the user was alone (i.e., sociability) by listening for conversation [3,54,84]. Some used it to detect if the user was sleeping if the surroundings were quiet (along with other sensor data such as light) [57,58].	+ Has utility in respect of social sensing. − Impacted by device placement − Relatively serious privacy concerns due to audio recording.
Phone lock/unlock status (SF)	Indicates whether the phone is locked or unlocked	It was used to infer phone usage behavior. By calculating the time between the unlock and lock states, studies estimated the phone usage time [24,25,91]. Additionally, this was also used as one of the factors to infer sleep (i.e., phone in locked state for long time during bedtime hours) [57,58,91]	+ Privacy-sensitive. − Unreliable by itself, used in conjunction with other sensors
Phone-call and text-message logs (SF)	Logs/records of text messages and phone calls	It has been used to infer the communication patterns of users, which correlate to social wellbeing. For example, decreased frequency of such communication features could indicate decreased sociability of individuals [55,69,85]	− Privacy concerns depending on what information is captured.
Screen status (S)	Indicates screen on/off status	Similar to phone lock/unlock status, it was used to infer phone-user behavior. Screen on/off indicated when the device was being used, which could further indicate distracted/anxious behavior [84], or infer sleep [19,91]	− Unreliable by itself, used in conjunction with other sensors − Can be impacted by phone notifications (resulting in screen on state)
Wi-Fi (S)	Indicates nearby Wi-Fi connectivity	These types of data were used as a complimentary source to infer location and indicated indoor mobility [8,51,60,88]	+ Can increase accuracy of location determination