Machine Learning-Aided Band Gap Engineering of BaZrS3 Chalcogenide Perovskite.

The non-toxic and stable chalcogenide perovskite BaZrS3 fulfills many key optoelectronic properties for a high-efficiency photovoltaic material. It has been shown to possess a direct band gap with a large absorption coefficient and good carrier mobility values. With a reported band gap of 1.7-1.8 eV, BaZrS3 is a good candidate for tandem solar cell materials; however, its band gap is significantly larger than the optimal value for a high-efficiency single-junction solar cell (∼1.3 eV, Shockley-Queisser limit)─thus doping is required to lower the band gap. By combining first-principles calculations and machine learning algorithms, we are able to identify and predict the best dopants for the BaZrS3 perovskites for potential future photovoltaic devices with a band gap within the Shockley-Queisser limit. It is found that the Ca dopant at the Ba site or Ti dopant at the Zr site is the best candidate dopant. Based on this information, we report for the first time partial doping at the Ba site in BaZrS3 with Ca (i.e., Ba1-xCaxZrS3) and compare its photoluminescence with Ti-doped perovskites [i.e., Ba(Zr1-xTix)S3]. Synthesized (Ba,Ca)ZrS3 perovskites show a reduction in the band gap from ∼1.75 to ∼1.26 eV with <2 atom % Ca doping. Our results indicate that for the purpose of band gap tuning for photovoltaic applications, Ca-doping at the Ba-site is superior to Ti-doping at the Zr-site reported previously.

Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data.

The amount of home-based exercise prescribed by a physical therapist is difficult to monitor. However, the integration of wearable inertial measurement unit (IMU) devices can aid in monitoring home exercise by analyzing exercise biomechanics. The objective of this study is to evaluate machine learning models for classifying nine different upper extremity exercises, based upon kinematic data captured from an IMU-based device. Fifty participants performed one compound and eight isolation exercises with their right arm. Each exercise was performed ten times for a total of 4500 trials. Joint angles were calculated using IMUs that were placed on the hand, forearm, upper arm, and torso. Various machine learning models were developed with different algorithms and train-test splits. Random forest models with flattened kinematic data as a feature had the greatest accuracy (98.6%). Using triaxial joint range of motion as the feature set resulted in decreased accuracy (91.9%) with faster speeds. Accuracy did not decrease below 90% until training size was decreased to 5% from 50%. Accuracy decreased (88.7%) when splitting data by participant. Upper extremity exercises can be classified accurately using kinematic data from a wearable IMU device. A random forest classification model was developed that quickly and accurately classified exercises. Sampling frequency and lower training splits had a modest effect on performance. When the data were split by subject stratification, larger training sizes were required for acceptable algorithm performance. These findings set the basis for more objective and accurate measurements of home-based exercise using emerging healthcare technologies.

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings.

BACKGROUND: Unsupervised home exercise is a major component of physical therapy (PT). This study proposes an inexpensive, inertial measurement unit-based wearable device to capture kinematic data to facilitate exercise. However, conveying and interpreting kinematic data to non-experts poses a challenge due to the complexity and background knowledge required that most patients lack. OBJECTIVES: The objectives of this study were to identify key user interface and user experience features that would likely improve device adoption and assess participant receptiveness toward the device. METHODS: Fifty participants were recruited to perform nine upper extremity exercises while wearing the device. Prior to exercise, participants completed an orientation of the device, which included examples of software graphics with exercise data. Surveys that measured receptiveness toward the device, software graphics, and ergonomics were given before and after exercise. RESULTS: Participants were highly receptive to the device with 90% of the participants likely to use the device during PT. Participants understood how the simple kinematic data could be used to aid exercise, but the data could be difficult to comprehend with more complex movements. Devices should incorporate wireless sensors and emphasize ease of wear. CONCLUSION: Device-guided home physical rehabilitation can allow for individualized treatment protocols and improve exercise self-efficacy through kinematic analysis. Future studies should implement clinical testing to evaluate the impact a wearable device can have on rehabilitation outcomes.