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This research paper describes the design of a pill dispensing device that can assist people with physical or cognitive limitations in taking their prescribed medications. The design is based on the communication between two devices for the purpose of dispensing pills at a scheduled time and identifying if these pills had been properly consumed within a specified time frame. The two devices are based on Arduino RP2040 connect microcontrollers and implement several sensors in the aid of dispensing and detecting of pill consumption. The sensors implemented are an IMU, and distances sensors, such as an ultrasonic sensor and an IR proximity sensor, additionally a real time clock module and stepper motor have been included in the design for the scheduling and dispensing of the pills. The two devices will communicate using Bluetooth for low energy devices (BLE) and the purpose of the devices is to provide aid to the intended target audience in achieving a healthier lifestyle. 

This research paper describes the design of a device that can assist seniors or people with physical or cognitive limitations to take their prescribed medications that are in the form of pills on time while verifying that such pills have been actually consumed. The design consists of a portable smart pill dispenser that will rest on a base, allowing it to dispense pills into a smart cup. The smart pill dispenser uses a stepper motor to rotate to a desired pills based on a specific time slot/day of the week. The smart cup attached to the pill box uses an accelerometer, gyroscope, and an IR proximity sensor to detect if a user is taking the medication by how much the smart cup is lifted and tilted. The smart cup will inform the smart pill dispenser if the pills are properly consumed or not, thus, allowing the device to potentially aid the patients to have a healthier life. 

Few-shot machine learning attempts to predict outputs given only a very small number of training examples. The key idea behind most few-shot learning approaches is to pre-train the model with a large number of instances from a different but related class of data, classes for which a large number of instances are available for training. Few-shot learning has been most successfully demonstrated for classification problems using Siamese deep learning neural networks. Few-shot learning is less extensively applied to time-series forecasting. Few-shot forecasting is the task of predicting future values of a time-series even when only a small set of historic time-series is available. Few-shot forecasting has applications in domains where a long history of data is not available. This work describes deep neural network architectures for few-shot forecasting. All the architectures use a Siamese twin network approach to learn a difference function between pairs of time-series, rather than directly forecasting based on historical data as seen in traditional forecasting models. The networks are built using Long short-term memory units (LSTM). During forecasting, a model is able to forecast time-series types that were never seen in the training data by using the few available instances of the new time-series type as reference inputs. The proposed architectures are evaluated on Vehicular traffic data collected in California from the Caltrans Performance Measurement System (PeMS). The models were trained with traffic flow data collected at specific locations and then are evaluated by predicting traffic at different locations at different time horizons (0 to 12 hours). The Mean Absolute Error (MAE) was used as the evaluation metric and also as the loss function for training. The proposed architectures show lower prediction error than a baseline nearest neighbor forecast model. The prediction error increases at longer time horizons.