Review of Wearable Devices and Data Collection Considerations for Connected Health

Abstract

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer's physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient's functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Keywords: deep learning (DL); digital healthcare; neural network (NN); quantified self (QS); wearable technology.

Figure 1

Various sensors can be attached to the body for motion capture, such as Smart Shoe, Pressure mat, sensors attached to leg, smartwatches, head sensors, data glove and Biometrics Goniometers and Torsiometers on toe and arm [15,16,17,18,19].

Figure 2

Various sources of data that may be used in a clinical trial.

Figure 3

(a) IMU sensor chip and (b) DOF for IMU movement [31].

Figure 4

The architecture of a complementary filter.

Figure 5

Musculoskeletal changes for (a) RA and (b) AS [64,65].

Figure 6

Wearable devices and their attached locations on the human body [71].

Figure 7

Sensor data were collected for several ranges of movement tests. The data contains random noise and movement for several tests, which require segmentation and extraction before analysis can occur.