Key Description and Design

The central focus of the device is to monitor the patient’s activity after having a stroke so that the best rehab exercise can be curated. Ideally, the part of the device collecting the data will be at most 5 cm. The patches that are supposed to adhere to the patient would make the device somewhere around 12-13cm. This way, we can also ensure the appeal of the device – valuing the fact that the device is both discrete and comfortable, minimizing any inconvenience to the patient. The goal is to make the device lightweight and non-bulky so it won’t interfere with the patient’s daily life while still providing valuable data for rehabilitation.
Our device would work similarly to the Zio XT device described in patents US9775520B2 and US8538503B2, where a computer-implemented system is worn by the user for an extended period of time, and the patient and a medical professional can review the data collected by the device. For our device, an activity monitor is associated with a patient and is configured to sense the patient’s body mechanics, both subtle and significant movements. A mobile device is paired with the activity monitor to execute an application for detecting the potential areas the patient needs to work on to optimize their recovery speed and efficiency. Electrocardiography (ECG) and physiological sensor monitors will be placed on the ankle, balls of the feet and secured to the patients toes and mid foot. These sensors will capture action potentials and muscle signals that the activity monitor may be unable to detect. When the ECG and physiological sensor detect relevant signals, the data will be uploaded directly into the patient’s electronic medical record for review by their physician. The app will also show patients the best exercise to maximize recovery.
Another function of the app that could prove to be beneficial is it’ll tell the user the projected time they might expect to regain most, if not all of their range of motion and their other facilities when following the exercises given to them by the app and/or their physician.
Considering the fact that strokes tend to happen to older individuals (70 or older), having a device that can help doctors and therapists formulate a plan to lower discomfort and soreness, regain the patient’s physical strength, and improve their range of motion while also maximizing efficiency is not simply ideal, but imperative. According to Wenwen Qi, in an article published by Wiley Online Library, an American multinational publishing company focusing on academic publishing and instructional materials, Wearable Internet of Things (IoT) devices equipped with sensors are increasingly used in rehabilitation to collect and transmit physiological data for processing and analysis. These devices offer significant potential in both rehabilitation and health monitoring by providing personalized, data-driven therapeutic interventions that can accelerate recovery and improve overall outcomes. More specifically, these everyday wearable devices help with rehabilitation, fitness training, and health monitoring in sports rehabilitation medicine. The therapy involves improving respiratory efficiency, lowering discomfort and soreness, and regaining muscle strength and joint range of motion. These everyday wearable devices help with rehabilitation, fitness training, and health monitoring. Qi also notes that the IoT “can speed up patients’ recovery and rehabilitation with more “precise,” “practical,” and “customized” rehabilitation therapies. While sports rehabilitation typically focuses on enhancing performance and preventing injury, the same principles of personalized, data-driven therapy can be adapted to support stroke patients in regaining function, strength, and mobility. By tracking key metrics—such as movement and muscle activity—this device can provide crucial insights into the patient’s recovery process, offering a more precise, efficient, and customized approach.

Figure 1: An early design sketch of the Stroke Sock™, featuring placement of sensors and overview of the material distribution.

Design Material

Choosing the correct materials for our device was imperative for its proper function due to its more complex shape. Considering the requirement that the device must shape around the user’s ankle and toes, the most logical solution was to use a felt-like material to tie everything together. It was important that the material was a type of fabric that was stretchy and flexible, while still being biocompatible, hypoallergenic and a tight fit on the user. The fabric that fit this description most closely was Lycra material, also known as spandex: a synthetic and elastic fabric (Gupta). Lycra material is also capable of being coated in a water-repellent coating, allowing its ease of use in environments containing water. While our device is unable to be worn effectively in water for long periods of time, this coating helps remedy issues with moisture in environments with rain, or in the shower.
It is also necessary to ensure that the device’s sensors wouldn’t be uncomfortable to the user, and that they would be resistant to water and external damage. One solution to this problem is to pad the outside of the sensors with a soft, elastic and waterproof material. Silicon is a waterproof and cost-effective material that is quite compatible with human skin. This material is also quite light and lacks rigid edges, allowing for comfort in the user’s daily activities. Even though the sensors are embedded in the spandex of the sock, this silicon coating is necessary to ensure that permeating moisture does not reach the sensors. The hydrophobic qualities of silicon ensure that the components it protects will not be damaged by moisture. Methyl groups attached to silicon-oxygen polymer chains are nonpolar, which repel polar water molecules and prevent the material from absorbing considerable amounts of water. (Team Xometry, 2022).
Furthermore, costs of silicon plating for the sensors must be considered, but this is negligible as silicon molding is a straightforward and inexpensive process. To streamline manufacturing, many companies offer silicon molding services that may be used in conjunction with our product in order to produce them in greater quantities. (Enrique, 2024)
Finally, the device must conform to the user’s lifestyle. Many people have activities involving some form of physical exertion: for example, exercise, swimming and hiking: all of which are activities that require proper equipment and flexibility to perform. Users of the device will need to feel comfortable while using the device, both in their physical therapy and daily activities, therefore that the device is versatile and easy to move in. All components of our device are compatible with motion, as both Lyra material/spandex and silicon are lightweight, easy to deform, and lack rigidity. While the Stroke Sock™ does contain components that are rigid, such as the various sensors and bluetooth components, these are firmly held to the user’s foot with the tightness of the spandex material, and the silicon plating prevents abrasion with potential sharp edges of these components.

 

Figure 2: 3D render of the Stroke Sock™ on a user, with the ankle and toe attachments pictured. The sensors, depicted as the rectangular portions, are exposed in this render for clarity.

Design Testing

For our product, we’d be testing two things. One of which is the overall functionality of the device and the connection between the device and the app. As mentioned previously, this device would measure various metrics relevant to stroke rehabilitation, such as gait, which is the displacement of the ankle in comparison to the force applied to the foot. We’d test the device’s overall functionality by following three different environments where subjects would use the device. The first is a “home” setting, where they’d do things such as lounge around, sleep, shower, etc. Next, we’d have a casual environment where subjects would go to work or outside for walks or meet up with friends. Our final environment would be an active environment, where subjects would be at the gym, going on runs, hiking, etc. These trials would last for around 3 months. It’s important to note that within the first month of stroke rehabilitation, recovery will progressively occur quicker than later (Lee et al., 2015).
For the first environment, we’d follow the subject’s “at home” setting, where subjects would do things such as lounge around, bathe, eat, sleep, etc. In this environment, we’d pay close attention to their sleeping habits post-stroke and see how they affect the app’s connectivity/functionality. Post-stroke, around 20-50% of patients are likely to suffer from some sleeping disorder, particularly insomnia (Hepburn et al., 2018). This is an alarming number, considering how important sleep is for regaining motor function and control over areas affected by stroke. It’s well known that sleep deprivation can lead to many negative consequences. We’d look at the app and see if the sensors picked up on any movement or leg jerking during normal sleep patterns. We could also see if the subject wakes up and steps out of bed on the limb affected by the stroke.
For the second environment, we’d examine gait levels more closely, testing whether the subject is leaning or limping on the unaffected limb for support. If the subject is walking stairs, we’d see if they move slower or apply more force to the ground due to a lack of control or sensation in their limbs.
For the third environment, we’d follow the test subjects’ active routine. Depending on the severity of the stroke, some subjects may still be able to go for runs, hike, or take long walks. We’d monitor how these varying levels of exercise affect their overall mobility and how long they can actively be mobile. During rehabilitation, physicians may recommend various forms of exercise to aid in rehabilitation. Some forms of exercise include stretching, yoga, (light) weight-related exercises, and gait training. Gait training is specifically aimed at assisting the individual with gaining mobility. These exercises are catered to each individual, so they are unique. Depending on the severity of the stroke, some people may have to undergo training (Lee et al., 2015). We’d monitor how the varying levels of exercise affect gait levels and determine if the exercise recommended by physical therapists benefits the patient.
When testing this product, reducing and avoiding all kinds of bias is important. Bias is a clear, unequal favoring of one group, thing, or niche compared to the other presented group(s). When testing products, biases come up in many ways. In the medical field, one of the most prevalent forms of bias is the halo bias, which uses one specific trait about someone to paint an overall picture of them. (Perera, 2023) For instance, if someone is obese, doctors are more likely to view them as someone who doesn’t have much responsibility or willpower in comparison to those who aren’t (Gopal et al., 2021). When conducting a trial for our product, we need to diminish any possible sources of bias. To do so, we must ensure that the design is created to accommodate all genders, sizes, and colors. For example, if we make the product while only considering the stereotypical male anatomy, it wouldn’t work as effectively for a female since their anatomical composition is different. Also, it wouldn’t work for men of different heights, weights, and perhaps ethnic backgrounds. Reducing bias is something that starts from the design. After this, we’d conduct a trial using stratified sampling. Stratified sampling is when people are divided into subgroups based on varying characteristics such as age, gender, ethnicity, etc. (Perera, 2023). Afterward, a random number of people from each strata is chosen to participate in the trial. This way, we can ensure that our pool of participants is diverse, which can give us helpful feedback when revising the product and testing its effectiveness overall.

Figure 3: Sample display of the paired app for this device. Pictured is the screen for the Gait Sensor. Different graphs are able to be displayed based on the components pinged, giving the user and doctor a sense of progress.