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How Machine Learning-Enabled Prosthetic Limbs Improve Mobility for the Disabled

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How Machine Learning-Enabled Prosthetic Limbs Improve Mobility for the Disabled


With the growth of artificial intelligence and machine learning in healthcare, even prosthetic limbs are becoming smart.

These smart prosthetics can combine manual control with machine learning for more accessible and effective use.

We are seeing a growth of machine learning in healthcare, where it is used to improve a patient’s overall health, including providing accurate diagnosis and better treatment plans. Additionally, machine learning (ML) can also understand healthcare data by improving diagnostics and predicting accurate outcomes. One of the latest fields where AI and ML have been making an impact is prosthetics. By creating smart prosthetics, machine learning can enable people with disabilities to function near-normally.

How Smart Prosthetics Work

Like a real set of limbs, smart prosthetics aim to understand muscle signals and perform a function according to the wearer’s choice. Let’s suppose an ML-enabled prosthetic arm is worn by an amputee with no lower arm. Using their upper arm, the person will be able to lift the prosthetic limb and direct it towards an object they wish to grip. A traditional prosthetic arm here will be able to locate the object but fail to place a firm grip upon it. An ML-enabled prosthetic limb, on the other hand, has been trained to observe muscle signals with regards to various motions and grips and can have a firm grasp on the object based on the area of contact. It can accordingly provide the best grip and also change its grip in case the object appears to be falling. Once the object has completed its use, the smart prosthetic device can use similar muscle signals to release the object.

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Challenges of Smart Prosthetics 

ML-enabled prosthetics can be far easier and more effective in use than regular prosthetic limbs. But even as machine learning in healthcare is becoming a reality, various challenges are associated with creating smart prosthetics.

Used by a Small Population

As per The Amputee Coalition, nearly 2 million people live with limb loss in the US, making them 0.60698% of the total population. With only a small percentage of people requiring prosthetics, there has been little investment to create better and more effective prosthetic limbs for amputees.

Costly in Production

Meeting the demands of the small population requires high costs of investment as prosthetic limbs tend to be costly in production. Additional investments in AI and ML-enabled prosthetics could significantly raise the costs of production for these prosthetic limbs.

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Yielding Low Return on Investment

Even with the creation of ML-enabled prosthetics, there are several risks associated with insurance companies reimbursing for such machinery, as they are highly expensive and have a high degree of abandonment.

Pending FDA Approvals

Another challenge for smart prosthetic devices is pursuing FDA approval and taking them to a private company. The delays in approval cause a slowdown in the technology, during which newer and more effective technologies are already in place to replace them.

 

Once the challenges mentioned above are addressed, smart prosthetic devices could be poised to take over prosthetic limbs. Machine learning in healthcare does not seem to be slowing down and it is the most inclusive technology to make a significant impact in the future of healthcare.



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TECHNOLOGY

The Role of Big Data Analytics in Accounting

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The Role of Big Data Analytics in Accounting


Companies generate enormous amounts of data that need to be processed to produce readable insights and outcomes.

Big data analytics in accounting is a game-changer as it’s improving risk identification and real-time access to data and reporting.

More firms are increasingly adopting newer technologies to make them more efficient. This includes blockchain, artificial intelligence, machine learning, robotic process automation, data analytics, etc. The use of traditional accounting has disrupted the world of accounting, but with the onset of big data analytics, it has gone leaps and bounds, tapping into the untapped potential of any business.

Use Cases of Big Data Analytics in Accounting

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Businesses accumulate tremendous amounts of data that could go into petabytes and zettabytes. The accounting function in any organization records all types of financial and non-financial transactions, collects them and analyzes them using predictive models to find actionable insights. Data analytics is all about making sense of the data received and thus, it takes away the hassle of traditional accounting. Let’s dive into why you would need to transition your business from using conventional to big data analytics.

1. Real-time Reporting

One of the biggest USPs of using big data analytics in accounting is its real-time reporting functionality. Most of the analytical tools available today are cloud-based, making real-time insights and reporting more accessible than ever. As big data deals with a trove of data, it crunches historical data in terabytes and even petabytes to find actionable insights.

2. Real-time Access

Another characteristic of using data analytics in accounting is real-time access. As it is cloud-based, it has the upper hand in timers of data visibility across different functions in an organization. It can be accessed concurrently, and different users can have different privileges for access.

Apart from that, the data syncs so that the changes made in one node are easily accessible on other nodes. This improved access to information in real-time with transparency makes decision-making easier.

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3. Risk Identification and Mitigation

Certain risk factors can prevent a business from outperforming the revenue it hit last quarter or against the rival. Big data can help find risks associated with financial services, such as the supply chain, fraudulent transactions or activities, liquidity, data breach, etc. Businesses can use all the data and add it to various algorithms to anticipate or predict possible outcomes or track fraudulent activities in the books. As accountants can now find errors and risks sooner, the chances of propagating from the point of no return diminish.

4. Data Visualization

Making sense of voluminous data is impossible without using tools such as Tableau. It is a heavily used data visualization tool for big data as it helps find the flow, pattern and irregularities in the dataset. Analyzing the visualized data can assist in making business decisions and strategies needed to adhere to in the future.

Conclusion

Big data analytics in accounting can be a significant driving force toward many use cases. It includes predicting sales performance on food, travel, hospitality and others across different data sources, such as Booking.com, Yelp, etc. It can reduce downtime and operational costs thanks to monitoring IoT sensor data.

Companies can use data analytics in accounting to zero fraudulent activities. Optimizing labor and staff requirements is another chunk of issue that can be curbed using big data based on prediction analysis.

Organizations worldwide are leveraging the power of big data analytics in accounting over the traditional approach. It is because of the many benefits that it brings to the table, including real-time data access and reporting, data visualization, data audits, and others.



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