# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Sensor-Based Systems
#### 4.1.2 Wearable Sensors
The reason behind using IMUs in such systems is their ability to accurately recognize the intensity, direction, and angle of movements conjugated with medication intake activity in a 3D coordinate system [117]. Collecting such data will help in modeling the user's physical activity and then infer it it is associated with medication taking activity or not. In [66], an eZ430-Chronos wrist module manufactured by Texas Instruments (Dallas, TX, USA), has been used to collect and transmit signals from the on-board tri-axis accelerometer. Signal processing and data classification for medication intake gestures recognition were used. The system achieved an accuracy of 96.7% when taking the medicine by two hands and 88% when taking the medicine by one hand. In [67; 118], data obtained from a 3-axis accelerometer and gyroscope of a Samsung Galaxy Gear smartwatch manufactured by Samsung Electronics (Yeongtong District, Suwon, South Korea) were employed to predict pill bottle opening, pill removal, pill pouring into the secondary hands, and water bottle handling activities. However, an algorithm has been employed to predict medication ingestion from the data obtained from the inertial sensors by recognizing two activities: detecting the motion associated with cap twisting while the smartwatch is worn on the wrist, and wrist rotation for the palm to face upwards when pouring pills from the bottle into the other hand. Using these algorithms, the authors predicted the medication bottle opening and palm up activities with 30% and 83.7% precisions, and 87.5% and 100% recalls, respectively. Similarly, in [68], accelerometer and gyroscope sensors embedded in a pair of smartwatches placed on both wrists of the user were used to sense and transmit readings associated with pill taking activity from 10 users. Using a decision tree classifier, the system was able to detect the wrist movement while taking medication with 78.3% accuracy using one smartwatch placed on either of the wrists. Moreover, the accuracy of the system was 86.2% when using two smartwatches for tracking the motion of both hands. [PERSON] et al. [69] used accelerometer data samples from wrist-watches and dynamic time warping technique to test if a sample belongs to either activities: taking a pill with water or drinking water and wiping mouth. Data from 25 individuals were used to classify the hand movement gestures associated with one of the previously mentioned activities. The system achieved 84.17% true positive rate. A further research study of [PERSON] et al. featuring wearable sensors presents a system for detecting two actions \"cap twisting\" and \"hand-to-mouth\" from a triaxial accelerometer and a gyroscope [70]. Classification accuracies were 95% and 97.5% for cap twisting and hand-to-mouth actions, respectively. Another application of accelerometers embedded in smartwatches is presented in [71]. One smartwatch placed on the right hand of the user was used to collect the acceleration data for the actions associated with medication intake. The achieved accuracy for putting pill in mouth was 100%. However, there was a significant confusion associated with the processes of opening pill box and drinking water actions. Hence, their approach requires the user to take medication using the same hand on which the sensor is placed. A medication tracking and reminder system, termed MedRem, was presented in [72]. Unlike other approaches that used IMUs available on smartwatches, MedRem uses the speaker microphone on a smartwatch to provide reminders and track medication adherence via voice commands. When reminders are provided in the form of voice commands, it is expected that the user send a recording via the microphone sensor to confirm or postpone taking medication. The smartwatch then uses an android speech recognizer to analyze user's input and update a server. The system is capable of recognizing native and non-native English speakers commands with 6.43% and 20.9% error rates. Finally, in a recent work, [PERSON] and [PERSON] [73] developed SmartMATES.