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Aman Shekhar
Aman Shekhar

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Training my smartwatch to track intelligence

#ai #machinelearning #techtrends

Ever found yourself staring at your smartwatch, thinking, “What if this little gadget could not just track my steps, but also my cognitive abilities?” Well, I’ve been exploring exactly that! After months of tinkering and learning, I’ve set out to train my smartwatch to track intelligence—yes, you heard that right!

It all began one lazy Sunday morning while sipping on my favorite brew. I noticed that while my smartwatch was doing a stellar job at counting steps and tracking my heart rate, it felt like it was missing out on the bigger picture—my brain’s performance. After diving deep into the world of AI and machine learning, it hit me like a lightbulb moment: why not leverage the power of AI to track and analyze cognitive functions through my daily activities?

The Premise: Measuring Intelligence

Let’s get one thing straight—intelligence is a complex beast. Ever wondered why some people can solve puzzles at lightning speed while others take their sweet time? We often focus on physical fitness metrics, but our mental agility is equally vital. So, I started with the idea of correlating various day-to-day activities to cognitive performance. After all, if my watch could monitor my mood, why couldn’t it analyze my brain power too?

Setting the Stage: Tools and Tech

I knew I needed a robust tech stack for this venture. My trusty smartwatch runs on WearOS, and I decided to use Python for data processing. I’m a huge fan of TensorFlow, so I opted for it to train my models. It’s like my best friend in the machine learning world!

The first step was to gather data. I used an API to pull in activity data from my smartwatch—steps taken, heart rate, sleep patterns, and even when I felt mentally sharp or foggy. I devised a simple setup that gathered this data daily. Here’s a snippet of the code I used to fetch activity data:

import requests

def fetch_activity_data(api_url, headers):
    response = requests.get(api_url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        print("Failed to fetch data")
        return None

api_url = "https://example.com/api/activity"
headers = {"Authorization": "Bearer YOUR_ACCESS_TOKEN"}
activity_data = fetch_activity_data(api_url, headers)
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Training the Model: The Fun Begins!

With the data in hand, it was time to train my model. I decided to use a regression model to predict cognitive performance based on the collected data. I can’t tell you how thrilling it was to see my model start to take shape! My first tests didn’t go as expected, though. I quickly realized my dataset was too small, and the variance in cognitive performance was a challenge.

After tweaking the data collection process, I added more features, such as the types of activities I engaged in—like solving puzzles, reading, or even playing games. I used this expanded dataset to retrain my model. Here’s a little snippet from my training code:

import tensorflow as tf
from sklearn.model_selection import train_test_split

# Assuming 'features' and 'labels' are prepared datasets
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=50, validation_split=0.2)
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Aha Moments: The Learning Curve

Let me tell you, the best part of this whole journey has been the “aha moments.” There were times when I felt like I was throwing spaghetti at a wall, seeing what stuck—like when I realized that the time of day affected my cognitive performance. Who knew that I was sharper in the mornings than late at night? It’s fascinating how our bodies and minds have their own rhythms.

Real-World Use Cases: What Did I Learn?

As I continued this project, I began using the insights from my smartwatch in real life. For instance, if my model predicted a dip in cognitive performance, I’d adjust my tasks—maybe I’d save intensive coding for the morning and leave the easier stuff for later. I even started tracking patterns; did I perform better on days I exercised or days I didn’t?

What’s more, I discovered that discussing findings with friends led to new ideas and improvements. I believe sharing knowledge is one of the greatest ways to grow in tech.

Facing Challenges: Troubleshooting Tips

Of course, it wasn’t all smooth sailing. I faced challenges with model overfitting at one point. If you haven’t wrestled with that before, it’s like trying to fit a square peg in a round hole. My model was too accurate on training data but performed poorly on new data. To combat this, I had to introduce regularization techniques, which I found to be a game-changer.

For anyone looking to avoid pitfalls, I suggest validating your model often and keeping your dataset diverse. Trust me; it’ll save you a world of headaches!

Final Thoughts: The Road Ahead

So where do I go from here? I’m genuinely excited about expanding this project. I’m considering incorporating more data sources, like nutrition and social interactions, to see how they affect mental acuity. The possibilities are endless!

In my experience, the tech journey is all about curiosity and experimentation. For those looking to embark on similar paths, take the leap. It’s okay to fail and learn. The blend of AI and wearable tech might just lead to a revolution in cognitive analysis.

So, what’s next for you? Have you thought about how you can push your technology further? Let’s keep the conversation going. After all, we’re all in this tech adventure together!

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