Date of Award


Degree Type


Degree Name

Master of Science in Electrical Engineering (MSEE)


Electrical, Computer, and Biomedical Engineering

First Advisor

Yan Sun


The new wave of wireless technologies, fitness trackers, and body sensors have had a great impact on personal biometric tracking and monitoring. These technologies make a great contribution to personal health care, and can even be used in clinical settings. Among all of these devices, smartwatches are one of the most popular, and are becoming increasingly common among the general public. Commercially available smartwatches incorporate sophisticated algorithms and multi-sensor technologies, which are capable of providing users with real-time biometrics. Some of these sensors include a photoplethysmography (PPG) sensor that detects the wearer’s heart rate, Galvanic skin response sensors which can provide skin surface information, and an accelerometer which can be used to provide activity and movement information. When considering clinical applications, researchers find the smartwatch’s PPG sensor to be of most interest, as heart rate is one of the most important vitals that are monitored for clinical purposes. Heart rate can be used to detect and prevent serious diseases, such as cardiovascular diseases and seizures. However, the accuracy of PPG sensors still needs thorough investigation. Although the ability of wearable PPG sensors to reliably measure heart rate in regular movement (i.e. walking or jogging) has been demonstrated in previous research, there doesn’t exist premier research that focuses on the accuracy of a PPG sensor in daily activities, such as brushing one’s teeth, cooking, or vacuuming. These activities are of interest because they involve short periods of high frequency vibrations or intense wrist movements, which could affect the smartwatch’s heart rate calculation. To validate the relative accuracy of a smartwatch’s PPG sensor in these activities, a Microsoft Band (MB) and a Huawei Android smartwatch (HW) were used to conduct a series of experiments from which the heart rate signals were gathered and evaluated. Six participants were recruited to collect data from these two smartwatches, which involved completing a set of three daily activities under a specific protocol. The participants completed these sets of activities twice, giving us enough data to compare the collected heart rate between the two watches. Each activity was further divided into different stages, including the Rest Stage, Dominant Hand Active Stage (D-Active Stage), and Non Dominant Hand Active Stage (N-Active Stage). The heart rate differences between each watch during the same activity and the same stage of all activities were evaluated. We also investigated how relative heart rate accuracy was affected by skin tone, and if we could tell which hand the watch was being worn, being the user’s dominant or non dominant hand.

During the experiment, each subject wore a MB and a HW on the wrist of their dominant hand. Care was taken to follow proper wear guidelines as suggested for each device in order to collect the most reliable data possible. Each participant did a series of timed activities including cutting vegetables, electric tooth brushing, and walking along a given route. The participant was asked to follow timed instructions from the experiment instructor. The heart rate measurements of the two devices were stored in separate CSV files in their Bluetooth-connected smartphones to be processed for further analysis. After a close examination of the experiment’s results, the vegetable cutting activity showed the largest heart rate differences among two devices, and the Dominant Hand Active Stage of cutting vegetables had the largest heart rate difference. Among all three test cases, electric tooth brushing shows the smallest heart rate difference in both the rest and active stages, which indicated that the influence of high frequency vibration is smaller than the magnitude of movement. Statistical results show that the user’s relative heart rate accuracy will be affected by daily activities even when a smartwatch is being worn on their non dominant hand. However, the influence is much smaller than if the watch is worn on the wrist of the user’s dominant hand. Furthermore, the skin tone of the participant also shows some effect on the relative accuracy of optical heart rate sensor as well.

Based on the findings of these experiments, we discovered that a further exploration of the heart rate anomaly detection algorithm is required. This algorithm was used to identify the anomaly in the smartwatch’s heart rate measurement while the user was completing an activity. The heart rate from the MB was compared with a pulse oximeter in order to tune the parameters of the anomaly algorithm. Data received from a separate test stage showed that the anomaly detection algorithm with tuned parameters can detect most of the heart rate anomalies identified by an examination of the heart rate signals.



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