In Healthcare, a dead battery isn’t just an inconvenience – it’s a clinical threat. From defibrillators and infusion pumps to implanted devices and patient monitors, every second of reliable power can mean the difference between life and death. When batteries fail unexpectedly, patient safety is immediately at risk. 

A study conducted in clinics in Europe, the US, and Canada on 1,054 subcutaneous cardioverter-defibrillator (S-ICD) devices found that approximately 3.5% of the devices experienced premature battery depletion over a 49-month observation period. The burden on business is shocking: equipment downtime due to maintenance or power issues carries steep costs, as some reports estimate hospitals can lose upwards of US$760 per device per day when medical equipment is idle. 

The challenge 

Our client challenged us to build an AI-driven solution that utilizes device logs to estimate the discharge trajectory and predict low-battery/depleted events in real-time across heterogeneous operating environments (e.g., variations in temperature, humidity, and workload conditions), with a secondary goal of identifying abnormal behaviour in charge/discharge cycles. 

Medical devices (like infusion pumps, ventilators, monitors, defibrillators, etc.) produce logs that record things such as power usage, charging status, battery level, and operational events. 

Our AI-driven system has the following innovative characteristics: 

1. Accurate predictions and timely alerts 

The system doesn’t just display battery levels, it projects usage trends. By learning how fast a device discharges, it can estimate when a critical threshold will be reached. For example, if a ventilator drops from 100% to 80% in two hours, the AI forecasts the exact time it will get to 20%, sending an early alert so staff can take action before the power runs out. 

1. Adaptable and anomaly-aware 

The AI-driven system adapts to heterogeneous operating environments, such as variations in temperature, humidity, and workload, and continuously monitors for irregular discharge patterns. Suppose a pump’s battery that usually lasts six hours suddenly drops to two hours. In that case, the system flags it as abnormal, helping identify potential issues such as battery wear, faulty hardware, or charging errors. It will alert clinicians or maintenance teams before a device runs out of power. It can send a text like “Warning: This infusion pump will hit 10% battery in 45 minutes.” 

1. Turning complex data into actionable insights 

Even with heterogeneous, noisy data from diverse operating environments, the system can learn patterns, predict remaining useful battery life, and detect anomalies. What once was scattered data now becomes a reliable foundation for proactive maintenance, operational efficiency, and safer device performance. 

While variability across environments makes the dataset noisy and inconsistent, it also provides a rich foundation for building AI models that can learn discharge patterns, predict remaining useful battery life even from partial cycles, and detect anomalies in charging or discharging behavior. 

Approach and solution 

We had to treat the problem as a timeseries prediction with two primary outputs: 

1) Discharge trend estimation to extrapolate progression toward low /depletion thresholds. 

2) Event prediction for lowbattery/depletion within a configurable time horizon. 

To achieve this, both general machine learning and deep learning models can be evaluated on sequences derived from the logged signals. The design should remain modular and flexible, allowing for iterative improvements and refinements as more data becomes available and logging coverage expands. 

We built a lean time series modelling pipeline designed to predict battery performance from device logs. The system ingests signals such as application voltage, temperature, environmental or contextual indicators (where available), and device state information. 

Using windows of these signals, along with simple derived trends, we trained the models to estimate how close the battery is to critical, low or depleted thresholds and to generate early warnings within an operational time horizon. 

In addition, we developed a lightweight inference prototype that consumes recent log windows and produces both predictions and thresholded alerts with confidence scores, enabling real-time monitoring and proactive intervention. 

The result 

The AI-Powered end-to-end system ran successfully and has also demonstrated the ability to deliver reasonable discharge trends under observed conditions, demonstrating clear potential for real-world application. Initial results highlight areas for refinement, as early warning precision/recall and time-to-depletion estimates require further optimization to meet operational tolerances. 

Performance was inherently stronger on well-represented devices and conditions, suggesting that expanding log coverage will significantly improve generalization. Our findings provide a solid foundation for iterating on model calibration, broadening the dataset, and enhancing robustness for production readiness. 

AI-Driven Battery Intelligence for Medical Devices 

In Healthcare, a failing battery is a risk to patient safety. Our AI-powered system predicts low-battery and depletion events in real-time, providing clinicians and technicians with early warnings before a device si reaching dangerous battery levels during critical care. 

By forecasting discharge trends, the system optimizes charging schedules, lowers downtime, and secures life-saving equipment is always ready when needed most. Beyond alerts, it identifies abnormal charging or discharging patterns (early signs of battery degradation or device malfunction), allowing teams to operate proactively and extend the medical assets lifespan. 

The professional, ethical and business impact of our AI-powered system: 

1. Stronger safeguards for patient safety 

1. Higher device availability in critical settings 

1. Decreased risk of emergency equipment failure 

1. Reduced long-term maintenance costs 

Beyond Healthcare: Industry-Wide Applications 

The same intelligent system that protects patients can also guard missions, operations, and infrastructure across industries where batteries power critical assets: 

1. Aerospace & Defense: Drones, satellites, and field equipment depend on precise power forecasting to guarantee mission success. 

1. Transportation & Logistics: Electric vehicles, delivery drones, and warehouse robots gain predictable uptime with optimized charging. 

1. Energy & Utilities: Renewable storage systems stay durable and reliable. 

1. Manufacturing & Industrial IoT: Battery-powered sensors and handheld tools ensure operations run smoothly. 

1. Consumer Electronics: Smartphones, laptops, and wearables deliver longer, more reliable performance with smarter battery insights. 

In conclusion 

Our approach provides a viable and practical solution for predicting real-time low battery and depletion levels from device logs. While the current dataset’s size and variability limit absolute accuracy, the pipeline already delivers valuable trend insights and establishes a solid foundation for production-ready deployment. With expanded log coverage and fine-tuned calibration, this solution can achieve full reliability while maintaining a lightweight footprint suitable for medical device operations. 

This is where AROBS’s 20+ years of embedded software excellence make the difference: transforming complex device data into solutions that protect patients, safeguard operations, and extend the life of critical assets. 

If reliability, safety, and efficiency are priorities in your organization, let’s make it happen. Contact AROBS today and unlock the power of AI-driven, compliance-ready solutions for your devices and operations.