SENSE - Using FSRs to Help Level I and II GMFCS of Cerebral Palsy

Project Context and Info:

While researching different disabilities/impairments, we identified a critical gap in assistive technology, specifically in Cerebral Palsy (CP). Most devices are confined to clinical/home use and lack real-time utility. This global issue is significant, as Spastic-CP accounts for roughly 80% of all cases (Lavendar, 2016). This often results in complex motor challenges that hinder daily independence (Figure 1).

Identifying the Main Issue:

Current CP-stabilizers offer several long-term rehabilitation methods with a low effectiveness rate. This leaves a vast majority of the population, particularly the 60% of children struggling with significant hand-function problems (Arner et al., 2008), without a viable solution for daily interaction (Figure 2).

How We Plan to Address the Issue:

The primary objective is to bridge the accessibility gap for individuals with CP who require special mobility equipment (Figure 2B). Our goal is to engineer a low-cost, digital-intensive wearable that provides effective assistance while also recording previously inaccessible physiological data to train and eventually improve long-term care.

The SENSE Solution!

Inspired by the need for integration into daily life, we designed SENSE. We ultimately minimized hardware bulk to increase user flexibility and reduce costs so that the device can assist the user in navigating physical tasks, addressing the limited abilities seen in patients, while simultaneously capturing vital data required for a self-sufficient lifestyle by providing clinicians with a digital record of the user's individualized impairment to inform future treatments better.

Constants and Variables

Constants:

Input range (0–1023 ADC Units, native Arduino R4 WiFi resolution)

Pressure scale (1–200 Mapped ADC, normalized from raw ADC via code)

Number of FSR sensor nodes (5, fixed at multiple finger positions)

Buzzer alert frequency (fixed tone, Hz defined in firmware)

WiFi transmission protocol (HTTP/TCP to Google Sheets API endpoint)

Independent Variables:

Applied grip force at each FSR node (reflected as mapped units, 1–200)

User-defined pressure threshold (adjustable per session or user profile, set within the 1–200 scale)

Duration of grip event (seconds)

Dependent Variables:

Buzzer trigger state (active/inactive based on threshold breach on mapped scale)

Normalized pressure reading per FSR (mapped value, 1–200)

Grip pressure data packet transmitted wirelessly

Python-generated visualization output (graphs, trends over time)

Controlled Variables:

Glove fit and FSR placement (palm + multiple fingers)

Arduino R4 WiFi firmware and code logic

ADC-to-pressure mapping function (map(rawValue, 0, 1023, 1, 200), consistent across all sensors)

Test environment (consistent hand posture baseline, indoor setting)

Subject (same user per controlled trial to eliminate potential variance)

Phase 1: Planning

We reviewed general resources on cerebral palsy and existing assistive technology, confirming no affordable wearable grip-monitoring device existed for daily use. FSR and Arduino documentation confirmed hardware suitability (Figure 3).

Phase 2: Prototype

FSR sensors were embedded at the palm and finger positions, wired to the Arduino R4 WiFi (Figure 4). Raw ADC values (0–1023) were normalized to a 1–200 scale via map(). Values exceeding the user-defined threshold trigger the buzzer. Time-stamped data is transmitted wirelessly to Google Sheets.

Phase 3: Design

Firmware was developed in C++ to handle FSR-reads, mapping, and WiFi-transmission, featuring debounce timing to eliminate false positives (Figure 5). This data is instantaneously imported into Python to generate force-distribution graphs, providing a digital record of motor progress.

Results

Quantitative Analysis of Asymptomatic "Digital Signatures"

Clinical Benchmark: Through a series of five differently simulated test profiles, SENSE provides a quantitative basis of “digital signatures” to assess motor dysfunction. In the Asymptomatic control simulation, a mean pressure of 45 units at 4.2% CV was registered (Figure 6).

This data is indicative of the overlapping pressure lines expected in a healthy grip. It serves as the primary clinical benchmark for all subsequent diagnostic tests.

Statistical Quantification of Dyskinetic and Spasticity Symptoms

Dyskinetic Analysis (Figure 7): These trials reported extreme instability, with involuntary pressure spikes at 192 units (326% more than the desired pressure). The high 32.4% CV and low 0.12 coordination coefficient quantified the erratic independent finger movements programmed to mimic tremors.

Spascity Simulations (Figure 7): This profile was characterized by a 3.2s Release Latency, eight times slower than the healthy 0.4s reset. This was paired with a 0.82 sync score, representing the mathematics of a global, “fisted” clench during muscle catch simulations (Figure 7).

Measurable Fatigue and Impaired Force Scaling Deficits

Low Muscle Tone Simulations (Figure 7): The results revealed a failure to maintain the isometric force. The peak reached 81 units, while the key factor was the -2.2 units/sec Fatigue Rate. This showed that the rapid force collapse muscular endurance deficit is measurable.

Impaired force scaling simulations (Figure 8): The data collected showed an extreme “all or nothing” overshoot amounting to 206 units. This proved that, although the simulated user exerted significant force, the identified 28.1% variance in stability indicated impaired fine-tuned modulation.

Diagnostic Success and Real-Time System Latency

Categorization Accuracy: SENSE moved beyond subjective observation by processing simulated inputs into a high-resolution database. The system achieved a 94% diagnostic success rate in accurately categorizing the five different simulations over 50 trials (Figure 9).

Biofeedback Capability: The mathematical resultant allowed for high-speed processing with a system latency of <50ms. This minimal delay makes the device fully capable of providing critical biofeedback in real-time.

Analysis:

System Processing

Mathematical Categorization: SENSE successfully processed simulated inputs into a high-resolution database by identifying relevant mathematics from five distinct profiles. This allows for a 94% success rate in accurately diagnosing motor dysfunction types through digital signatures (Figure 9).

Real-Time Responsiveness: Because the system latency is less than 50ms, the device can process complex force data and provide immediate alerts. This high-speed response enables the user to receive real-time biofeedback to correct motor errors.

Prototype Functionality

Detection of Motor Deficits: The prototype identifies specific clinical symptoms, such as Release Latency and Fatigue Rates, that were previously restricted to subjective observation. By measuring the "all or nothing" overshoot and fisted clenches, the system proves it can quantify physical impairments (Figures 7 and 8).

Baseline Comparison: The functionality of the device relies on comparing real-time data against the established asymptomatic mean of 45 units. By using this benchmark, the prototype can instantly detect when grip pressure or coordination deviates from healthy standards (Figure 6).

Conclusions

From the previous results listed in Fig. 6-9, we can draw a number of conclusions.

First of all, SENSE is accurately demonstrating the differences in cerebral palsy types extremely clearly, making diagnosis much faster and easier.

SENSE was successfully able to measure pressure at every 100ms, leading to a very precise form of measurement to track minuscule points of data, which plays into the bigger picture for understanding cerebral palsy.

Cerebral palsy patients will have a more accessible way to log progress with multiple metrics. This opens a window for detailed analysis to medical professionals.

In addition, the SENSE app allows users to log sessions from home with minimal effort and to obtain instant results, leading to data that were not previously accessible, now being only a few clicks away.

Overall, SENSE has efficiently met the main objectives we had set out to address. SENSE provides an affordable and accessible assistive device on the market, which serves both the use of rehabilitation and daily-use optimization.

Next Steps

In terms of future directions, we intend to miniaturize the circuitry into a PCB (Printed Circuit Board) by replacing the breadboard, resulting in a smaller, sturdier, and wearable device.

We also intend to install additional nodes with FSRs so as to accurately measure the distribution of pressure on each finger. By conducting user studies with subjects exhibiting different severities of CP, we would be able to help SENSE develop pressure thresholds specific to the individual, thus increasing its relevance within the clinical setting.

Thanks to the many special people who made this project possible:

Ms.Ellis, our science teacher, for helping us expand our views on circuitry and science in general.

Dr. Ken Wilson, the Dean, and the Faculty of Science, Ontario Tech University, for sponsoring and providing us the opportunity to go to the Canada Wide Science Fair, to whom we are extremely grateful.

Ms. Olaveson, the Coordinator for the Durham Regional Science Fair for making this possible.

Ms. Jennifer Mackinnon (CWSF Delegate) for their supervision and guidance for the preparation of the CWSF.

The judges at both the Durham Regional Science Fair and the Ajax-Pickering Science Fair for important feedback and constructive criticism.

Our teachers at Pickering High School for the many skills we developed to be here.

Our parents, who continue to support and guide us through the process and in life.

Websites Used:

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www.cerebralpalsyguide.com/cerebral-palsy/types/.

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