LIFEBOX

Lifebox is a comprehensive digital health platform that merges emergency medical response with IoT-enabled vaccine monitoring to serve both urban, rural area and underserved communities.

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Inspiration: In many urban and rural communities, access to emergency medical care and reliable vaccine storage remains a life-threatening challenge. Patients often lose their lives due to:

i.Delayed response from hospitals or ambulances. ii. Lack of real-time data on facility or equipment availability. iii. Spoilage of vaccines and medicines due to cold-chain failures. Lifebox was inspired by the need to bridge this critical gap in healthcare systems. Through community outreach and field research, we witnessed how real-time coordination and automated monitoring could revolutionize emergency care even where internet and infrastructure are limited.

What it does:

Lifebox is an IoT-enabled emergency health platform that combines: i. Emergency Response Automation ii. Critical Care Resource Allocation iii. Vaccine and Medicine Cold Chain Monitoring iv. Automated Emergency Alert & Dispatch by Detects emergencies via IoT health sensors (e.g., pulse, oxygen saturation). v. Triggers alerts to ambulance teams and hospital staff. vi. Uses SMS, mobile apps, and GSM fallback to notify responders. vii. Uses a weighted formula to assign triage scores for prioritization: Triage Score = α⋅Heart Rate + β⋅Oxygen Level − γ⋅ Time since Incident viii. Ensures high-risk patients are treated first. ix. Automates bed, ventilator, and staff assignment. x. Tracking ICU/HDU beds, Oxygen cylinders Ventilators, Blood units. xi. Enables dynamic reassignment of critical resources during surges. B. Automated Monitoring System for Solar Vaccine Cold Chain i. Monitors vaccine storage temperatures using embedded sensors. T(t)=T ambient + (T0 − T ambient)e−kt ii. Ensures compliance with cold-chain range 2∘C≤𝑇≤8∘C iii. Issues alerts on over-temp or power loss events via GSM/Wi-Fi.

How we built it:

The development process involved combining several technologies into a single, modular battery powered system using i. Microcontroller: ESP8266 ii. Sensors: DS18B20 (temperature) iii. Battery Management for battery charging iv. Communication: SIM800L GSM module and ESP8266 for Wi-Fi v. Cloud: ThingSpeak vi. We used Arduino IDE, Vero boards, connector, switch, DC to DC converter

Challenges we ran into: Building the Automated

Solar Vaccine Monitoring Device involved several technical and environmental hurdles. i. Temperature Sensor Calibration Challenge: Sensor instability in humid/mobile conditions. Solution: Used industrial-grade DS18B20 sensors and software calibration with Arduino code. ii. Unstable Network Connectivity Challenge: GSM/Wi-Fi signal issues in urban and rural regions. Solution: Implemented offline logging with buffered alerts for delayed transmission. iii. System Integration Complexity Challenge: Synchronizing sensors, power modules, and IoT components within a compact design. Impact: Wiring issues, bugs, and boot failures. Solution: Used a modular Veroboard layout and consistent communication protocols

Accomplishments we are Proud Of

i. Successfully integrated multiple sensors and modules into a compact, field-ready system. ii. Developed a low-power embedded software architecture for uninterrupted performance. iii. Enabled real-time alerts for vaccine safety in locations without internet or grid power.

What we learned:

Developing Lifebox was a multidisciplinary learning experience, covering engineering, public health, and field logistics: i. Power Optimization Learned to choose low-power components and implement sleep modes. ii. Battery & Energy Management Understood how to extend system life with smart energy distribution. iii. Cold Chain Precision Realized that even minor temperature fluctuations can compromise vaccine efficacy. iv. Sensor Calibration is Crucial Accurate readings require proper sensor placement and redundancy. v. Real-World Deployment ≠ Simulation Had to address network instability, environmental noise, and unexpected interferences. vi. Built Resilient IoT Routines Implemented fail-safe modes, data buffers, and offline fallbacks for rural deployment. vii. Designed for Durability Enclosure needed to withstand dust, heat, vibration, and water exposure.

What's next for Lifebox: Lifebox is focused on expanding its global impact in surgical safety through several key initiatives:

i. Capnography Access: Rolling out more capnographs in low-resource operating rooms and advocating for global policy adoption.

ii. Nurse Leadership Training: Scaling programs to strengthen surgical teams, especially in Africa.

iii. Clean Cut Program: Expanding infection prevention practices to more hospitals across Africa and Latin America.

iv. Lifebox Light Distribution: Increasing access to surgical lighting in under-resourced facilities.

v. Pulse Oximeter Outreach: Continuing device distribution and influencing policy through research.

vi. Local Leadership: Prioritizing leadership from the Global South as part of its decolonization strategy.

vii. Innovation & Data: Investing in new tools and evidence-based programs through research and data-driven development.

Lifebox’s work is entering a new phase with an emphasis on equity, local ownership, and innovation in global surgical care. Math Behind Lifebox

  1. Emergency Response Time Optimization We model the best-fit hospital recommendation as a distance minimization problem under availability constraints. Let: • H={h1,h2,…,hn}H = {h_1, h_2, \dots, h_n}H={h1,h2,…,hn}: set of hospitals

• D(hi)D(h_i)D(hi): distance from user to hospital hih_ihi

• A(hi)=1A(h_i) = 1A(hi)=1 if hih_ihi has available bed & right specialist, else 0 We aim to: latex CopyEdit \min_{h_i \in H} D(h_i) \quad \text{subject to} \quad A(h_i) = 1 This ensures the user is routed to the nearest hospital with availability.

  1. Vaccine Temperature Monitoring Let: • T(t)T(t)T(t): temperature reading at time ttt

• Tmin,TmaxT_{min}, T_{max}Tmin,Tmax: acceptable temperature range (e.g., 2°C to 8°C)

• Δt\Delta tΔt: time interval of measurement

• NNN: number of intervals in a day

The spoilage risk score (S) can be estimated as: latex CopyEdit S = \frac{1}{N} \sum_{i=1}^{N} \mathbb{1}\left[ T(t_i) < T_{min} \, \vee \, T(t_i) > T_{max} \right] Where 1\mathbb{1}1 is the indicator function. A high value of SSS means high spoilage risk, triggering an alert.

  1. Offline Sync Latency Modeling Let: • nnn: number of unsynced records

• bbb: average record size (KB)

• sss: available bandwidth (KB/s)

• LLL: latency before full sync completes Then, latex CopyEdit L = \frac{n \cdot b}{s}

This helps determine sync delay and optimize queue size for rural facilities with intermittent connectivity.

  1. Secure Payment Verification Let: • PfraudP_{fraud}Pfraud: probability of payment fraud

• VVV: verification factors used (OTP, biometric, ID match, etc.)

• rrr: reliability score of each factor vi∈Vv_i \in Vvi∈V, such that r(vi)∈[0,1]r(v_i) \in [0, 1]r(vi)∈[0,1]

We can define a fraud mitigation function: latex CopyEdit P_{fraud} = 1 - \prod_{v_i \in V} r(v_i) The more reliable verification layers we use, the lower the final fraud risk. Example: If we use OTP (r=0.9r = 0.9r=0.9), biometric (r=0.98r = 0.98r=0.98), and hospital ID match (r=0.85r = 0.85r=0.85): latex CopyEdit P_{fraud} = 1 - (0.9 \times 0.98 \times 0.85) \approx 0.2522 Fraud risk drops to ~25%, and adding one more verification (say, PIN code r=0.95r = 0.95r=0.95) lowers it further to ~13%.

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