When the servos are not preventing movement, they rotate freely, sending positional data such as location and acceleration to the circuit board in the process. However, when the sensor scheme detects abnormal movement, the board sends a signal to a servo to lock and provide resistance to the user, reducing movement while acting as the joints of the robotic arms. The 3D printed arm was designed with a reinforced triangle lattice for a combination of strength, ability to organize wires, and reduced material cost. It has a full 360 degree range of motion due to the two actuating joints of the servos, allowing users to draw large circles. On one end of the arm is a pen holder made of 3D-printed natural flex filament which allows the arm to grip a pen, pencil, or marker in a large variety of sizes. Various improvements were made to the arm to increase stability, provide a wider range of writing utensils, and reduce sagging in response to issues caused by the arm’s length.Arduino utilizes a form of the C++ coding language to input, process, and output data. While C++ as a programming language is excellent for AI, Arduino Uno boards have very limited RAM memory, meaning they process data very slowly. This means that by our choice of hardware, our software design had very limiting parameters to take into account. Therefore, container raspberries our focus in creating this AI and the rest of the prototype’s software was to streamline the logic needed to reduce movement.
Our code simply took in the accelerometer data from the servo motors, determined if there was a large enough change between the previous reading, and the current reading, to necessitate a system reaction, which then determined which servo should lock to reduce the movement and carry out that action.This is the prototype we are currently working on. The main hardware changes are switching the old Arduino board for a Raspberry Pi, as well as using a more robust design of the 3D printed arm and casing. Additionally, we are in the early stages of adding additional sensors to the user’s arm to track their movement directly. These changes were caused by or in-turn caused software changes such as an improved AI that processes the increased information and a switch from C++ to Python for the coding language.Our new circuit board is a Raspberry Pi 4, which is a single board computer rather than a microcontroller like an Arduino. We chose to switch our processing unit from a microcontroller to an SBC because they are more reliable, are powerful enough to run operating systems, and can compute much faster than microcontrollers . We also 3D printed a new case to hold the Raspberry Pi PCB, as it is much larger than the Arduino Uno. These upgrades will make future software development feasible. In addition to the new board, we have incorporated two additional Raspberry Pi Pico microcontrollers which input sensor data from the user’s armbands. We chose these microcontrollers for the armbands because they work better with sensor schemes, are capable of bluetooth communication with the PCB, and are some of the smallest inexpensive boards available .
These two armbands will be placed near the wrist and inner elbow, allowing for an additional six degrees of sensing through their monitoring of sEMG, rotation, and acceleration through internal motion units and sEMG sensors.The software running on the Raspberry Pi is based in Python rather than C++. The main benefit of this is to improve the user experience as well as the readability of the code. When using an Arduino and C++, the servos must be calibrated every time the device is powered on, which increases startup time. With a Raspberry Pi and Python, the calibration code is modularized, running only on the initial startup. Once run, the calibration saves data to a JSON file which can be read the next time the device is booted. All servo and sensor settings are also written in the calibration code and imported by the main script, which greatly increases readability. However, due to the Raspberry Pi’s lack of an analog-to-digital converter , a Raspberry Pi Pico is required to read analog position data from the servos. The Pico is a microcontroller running MicroPython, and the code saved on it is a simple script that checks the servo position. When required, the Pi sends a request to the Pico for the servo positions, transmitted over USB serial connection. Overall the Python code greatly simplifies the process of testing/prototyping, as well as provides functionality for all future upgrades, such as AI or a display/user interface.This new system will require an AI run on our Raspberry Pi to take in the information from the armbands and process it simultaneously to the user’s muscles taking in the same input.
Future development in this area will focus on the speed of the data transfer, AI calibration to process the inputs, and creating a system to determine fatigue level from input. We also have plans to implement multiple drawing modes. Currently, our robot only stabilizes linear movement, however, we are looking into creating additional modes to stabilize curved motions and guide users through predetermined motions. We anticipate that the curved mode will allow for much greater professional functionality, as precise arc drawing already involves the use of multiple tools such as compasses or protractors. Programming the robot with predetermined motions will not only allow it to guide users through tracing activities that might be designed to work on specific muscles, but will also allow the robot to collect data on tracing accuracy if the user traces the shape without the servo’s locking-mode on.So far, all of our testing has been for feasibility—to determine if the robot works as intended and has the main elements it needs to achieve our goals through further development. Now that we are fairly confident that we have everything we need to broadly meet our standards, our focus will begin to shift towards testing and redesigning the robot with the intended users in mind. Since this robot is designed primarily to aid stroke rehabilitation patients, we have many plans to ensure our user experience in general is accessible to our users. We must make sure that the UX is comfortable, simple, and intuitive. Our initial steps to improve UX include designing the armbands to not impede movement as opposed to some slightly more accurate methods, to use adhesive sEMG sensors instead of needles to avoid unnecessary pain and medical risk, and to be easy to put on even with limb instability. We have plans to incorporate large, textured buttons as the power and modes switches to make it easier for users to access different functionality and a small touch screen to provide feedback. This is because older patients tend to have difficulty with complicated touchscreen controls, draining pots so we want to ensure that the device will have different levels of digital intractability based on user comfort . In addition, we will work to make the setup of the robot as simple as possible for the user. Through the continued development of this robot and the prioritization of potential patient needs, we hope to create a robot that will act as an elegant solution to various patient and provider needs for the stroke rehabilitation industry, in addition to providing a useful new tool to professionals and hobbyists alike.There is natural spatial variability present in vineyards due to the variations in soil characteristics and topography . With traditional destructive methods, it is difficult to obtain enough comprehensive information from the soil pits at the field scale. These soil characteristics may directly affect the water availability for grapevines, which eventually determine the physiological performance of the plants . However, there is no variable management practices currently available to accommodate the natural spatial variability. Thus, the spatial variability derived from vineyard soils will inevitably be expressed in the whole plant physiology at the cost of homogeneity of vineyard productivity and quality. We previously reported the spatial variation of midday stem water potential affecting grapevine carbon assimilation and stomatal conductance of grapevine . The resultant variations in whole plant physiology were associated to flavonoid composition and concentration at the farm gate. However, there is a lack of information about the effects on the chemical composition in the final wine, which would ultimately determine wine quality as perceived by consumers.
Georeferenced proximal sensing tools can capture the spatial and temporal variability in vineyards, making it possible to supervise and manage variations at the field scale . Previous studies showed that soil bulk electrical conductivity may be used to evaluate many soil attributes, including soil moisture content, salinity, and texture . Soil electromagnetic induction sensing has been used in precision agriculture to acquire soil bulk EC at the field scale due to its non-invasive and prompt attributes . Although research had been conducted on the relationships between soil electrical properties with plant water status, they were mostly point measurements and the results were rarely interpolated to whole fields. There were only a few studies that investigated the EMI sensing and soil-plant water relationships over a vineyard . Previous research suggested that the connection between soil water content and soil bulk EC could have relied on specific soil profiles, and needed to include soil physical and chemical properties to complete this connection . Nevertheless, there is evidence that soil bulk EC may still be useful not only to identify the variability in soil, but also in the plant response affected by vineyard soils such as yield, plant physiology, and grape berry chemistry . Plant available water is a determinant factor on grapevine physiology, together with nitrogen availability in semi-arid regions . Wine grapes are usually grown under a moderate degree of water deficits as yields were optimized at 80% of crop evapotranspiration demand with sustained deficit irrigation . Water deficits would limit leaf stomatal conductance and carbon assimilation rate that sustain grapevines’ vegetative and reproductive growth and development . When grapevines are under water deficits, carbohydrates repartitioned into the smaller berries would enhance berry soluble solids content . Sucrose and fructose, which are the major components of total soluble solids in grape berry, can act as a signaling factor to stimulate anthocyanin accumulation . The effects on grapevine physiology and berry composition also depend on the phenological stages they occur and how severe and prolonged the water deficits are . Flavonoids are the most critical compounds dictating many qualitative traits in both grape berries and wine . The variations in environmental factors could alter the concentration and biosynthesis of flavonoids and can be extrapolated spatially within the same vineyard, including water deficits , solar radiation , and air temperature . Among flavonoid compounds, anthocyanins are responsible for the color of berry skin as well as wine . Moderate water deficits during growing season can increase anthocyanin concentration in berry skin and wine . However, water deficits can impair plant temperature regulation through evaporative cooling . They may also inhibit berry growth by limiting berry size and altering berry skin weight . Thus, in some cases it may be uncertain if water deficit promotes anthocyanins biosynthesis or reduces berry growth, or contributes to anthocyanin degradation . Applying water deficit on grapevines can contribute to greater proportion in tri-hydroxylated over dihydroxylated anthocyanins due to the up-regulation of F30 5 0 H. Another major class in flavonoids, proanthocyanidins, are polymers of flavan-3-ol monomers and they contributes mainly toward astringency or bitterness in wine. Compared to anthocyanins, water deficits showed mild effects on proanthocyanidins . However, water deficits with great severity can still alter the concentration and composition of proanthocyanidins in both berries and wine . Selective harvest is one of the targeted management strategies to minimize the spatial variation in berry chemistry in vineyards . By differentially harvesting or segregating the fruits into batches prior to vinification, the berry composition can be artificially set at a more uniform stage with minimal variations . In our previous work, we reported the use of plant water status to determine the spatial variation of grape berry flavonoids . The goal of this study was to deduce if the spatial variability of soil bulk EC and differences in soil texture can be related to plant physiology and grape and wine composition.