Physical Activity & Fitness Analysis

Introduction

Physical activity is defined as the movement of the human body produced by muscle contraction that generates energy expenditure above the resting metabolic level, and it's closely related to physical exercise and fitness.

Physical exercise is a planned, structured and repeated body movement performed to maintain or improve one or more components of physical fitness. We understand "Fitness" as the set of attributes or qualities that people have or acquire to improve performance.

Nowadays, thanks to technology we can take an in-depth analytical look at my trainings: running styles, training habits, and achievements. Helped with a GPS fitness tracker you can analyze your workout routines to see how are you performing.

If lockdown has inspired you to lace up your trainers and get running, you’re not alone. And if you haven’t yet – as the days get longer and exercise restrictions are eased, it’s a great time to start.

Objetive

Runkeeper is an app that lets you keep track of your progress as your running journey goes. Users can see their stats to check just how far they’ve come, if you are a data enthusiast making a depth analysis would be an interesting way yo get some insights of your progress and performance, and that's exactly what we are going to do.

Data

Fortunately, the app counts with a handy import feature. It’s designed for people with activity data saved on other devices or systems but want to import those activities.

import pandas as pd
import matplotlib.pyplot as plt
import warnings
import statsmodels.api as sm
warnings.filterwarnings("ignore")
%matplotlib inline

workout = pd.read_csv('Workout_data.csv', index_col = 'Date', parse_dates = ['Date'])
display(workout.sample(3))
print(workout.info())

	Activity Id	Type	Route Name	Distance (km)	Duration	Average Pace	Average Speed (km/h)	Calories Burned	Climb (m)	Average Heart Rate (bpm)	Friend's Tagged	Notes	GPX File
Date
2013-04-13 08:51:29	2c971ecf-efdd-4a3d-be67-ba249aa5557a	Running	NaN	6.34	33:02	5:13	11.51	454.0	33	NaN	NaN	NaN	2013-04-13-085129.gpx
2016-12-06 18:36:37	159d92f4-c96b-43ea-88a3-8f1060d170ed	Running	NaN	6.99	41:34	5:57	10.08	500.0	49	139.0	NaN	TomTom MySports Watch	2016-12-06-183637.gpx
2013-07-19 18:40:13	0547ce8b-c7c8-40b6-bb71-feff6298c5be	Running	NaN	1.72	8:33	4:58	12.07	40.0	6	NaN	NaN	NaN	2013-07-19-184013.gpx

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 508 entries, 2018-11-11 14:05:12 to 2012-08-22 18:53:54
Data columns (total 13 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Activity Id               508 non-null    object 
 1   Type                      508 non-null    object 
 2   Route Name                1 non-null      object 
 3   Distance (km)             508 non-null    float64
 4   Duration                  508 non-null    object 
 5   Average Pace              508 non-null    object 
 6   Average Speed (km/h)      508 non-null    float64
 7   Calories Burned           508 non-null    float64
 8   Climb (m)                 508 non-null    int64  
 9   Average Heart Rate (bpm)  294 non-null    float64
 10  Friend's Tagged           0 non-null      float64
 11  Notes                     231 non-null    object 
 12  GPX File                  504 non-null    object 
dtypes: float64(5), int64(1), object(7)
memory usage: 55.6+ KB
None

Data Preprocessing

We hae columns that contain irrelevant data for our analysis, so we are going to get rid of them, addicionally we are going to look after missing values and fill them with the average value of the column.

cols = ['Notes', 'Calories Burned', 'GPX File', 'Activity Id', 'Friend\'s Tagged', 'Route Name']
workout.drop(columns=cols, inplace=True)
display(workout.Type.value_counts())
workout['Type'] = workout.Type.str.replace("Other", "Unicycling")
workout.isnull().sum()

Running    459
Cycling     29
Walking     18
Other        2
Name: Type, dtype: int64

Type                          0
Distance (km)                 0
Duration                      0
Average Pace                  0
Average Speed (km/h)          0
Climb (m)                     0
Average Heart Rate (bpm)    214
dtype: int64

cycle_avg = workout[workout['Type'] == 'Cycling']['Average Heart Rate (bpm)'].mean()
run_avg = workout[workout['Type'] == 'Running']['Average Heart Rate (bpm)'].mean()

walk = workout[workout['Type'] == 'Walking'].copy()
cycle = workout[workout['Type'] == 'Cycling'].copy()
run = workout[workout['Type'] == 'Running'].copy()

run['Average Heart Rate (bpm)'].fillna(int(run_avg), inplace=True)
cycle['Average Heart Rate (bpm)'].fillna(int(cycle_avg), inplace=True)
walk['Average Heart Rate (bpm)'].fillna(110, inplace=True)

run.isnull().sum()

Type                        0
Distance (km)               0
Duration                    0
Average Pace                0
Average Speed (km/h)        0
Climb (m)                   0
Average Heart Rate (bpm)    0
dtype: int64

Exploratory Data Analysis

Let's find out about the runnig metrics using a visualization of the five years the data was collected. We want a subplot for each metric, but with an idenpendent y axis for each one, the x axis will be shared.

run_13_18 = run['20190101':'20130101']
run_13_18.plot(subplots=True,
          sharex=False,
          figsize=(12,16),
          linestyle='none',
          marker='o',
          markersize=3)

plt.show()

It seems that by some reason Hearth rate has many missing values, that may be due to it wasn't being recorded. Looking at this metrics it may seem that we can look and find patterns just by sight, but We want to go further and get real numbers that can answer some common questions among fit people:

• How often do I train?
• How fast do I run?
• What is my average distance?

So, lets answer those questions by making some calculations. We are going to use data from 2015 to 2018 since it has the heart rate recorded.

Lets also plot long term averages of my distance run and my heart rate with their raw data to visually compare the averages to each training session.

run_15_18 = run['20190101':'20150101']

print('Running last 4 years average:')
display(run_15_18.resample('A').mean())

print('Last 4 years weekly average:')
display(run_15_18.resample('W').mean().mean())

weekly_training = run_15_18['Distance (km)'].resample('W').count().mean()
print('Average trainings per week:', weekly_training)

Running last 4 years average:

	Distance (km)	Average Speed (km/h)	Climb (m)	Average Heart Rate (bpm)
Date
2015-12-31	13.602805	10.998902	160.170732	143.353659
2016-12-31	11.411667	10.837778	133.194444	143.388889
2017-12-31	12.935176	10.959059	169.376471	145.247059
2018-12-31	13.339063	10.777969	191.218750	148.125000

Last 4 years weekly average:

Distance (km)                12.518176
Average Speed (km/h)         10.835473
Climb (m)                   158.325444
Average Heart Rate (bpm)    144.801775
dtype: float64

Average trainings per week: 1.5

run_dist = run_15_18['Distance (km)']
run_hr = run_15_18['Average Heart Rate (bpm)']

fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, figsize=(12, 8))

run_dist.plot(ax=ax1)
ax1.set(ylabel='Distance (km)', title='Historical data with averages')
ax1.axhline(run_dist.mean(), color='blue', linewidth=1, linestyle='-.')

run_hr.plot(ax=ax2, color='red')
ax2.set(xlabel='Date', ylabel='Average Heart Rate (bpm)')
ax2.axhline(run_hr.mean(), color = 'blue', linewidth=1, linestyle='-.')

plt.show()

Motivation is also important. And a visual of your goals can help you reach them. so lets say that we set a goal for 1000 km per year, through 2013-2018 lets see if we reach the goal marked by a star and dividing our graph in 3 areas.

Lets also answer how are we progressing in running skills by decomposing the weekly distance runned and visually compare it to the original data.

run_annual = run['2018':'2013']['Distance (km)'].resample('A').sum()

fig = plt.figure(figsize=(15.0, 6.0))
ax = run_annual.plot(marker='*', markersize=14, linewidth=0, color='#4361EE')
ax.set(ylim=[0, 1210], 
       xlabel='Years',
       title='Annual totals for distance',
       xlim=['2012','2019'],
       ylabel='Distance (km)')

ax.axhspan(1000, 1210, color='#CAFFBF', alpha=0.8)
ax.axhspan(800, 1000, color='#FDFFB6', alpha=0.8)
ax.axhspan(0, 800, color='#FFADAD', alpha=0.8)

plt.show()

run_dist_wk = run['2018':'2013']['Distance (km)'].resample('W').bfill()
x = sm.tsa.seasonal_decompose(run_dist_wk, extrapolate_trend=1, freq=52)

fig = plt.figure(figsize=(12, 5))
ax = x.trend.plot(label='Trend', linewidth=2)
ax = x.observed.plot(label='Observed', linewidth=0.5, color='#4361EE')
ax.legend()
ax.set(xlabel='Years',
       ylabel='Distance (km)')
ax.set_title('Running distance trend')
plt.show()

For our last metric visualization we are going to use the heart rate since a popular metric used to measure training intensity. Depending on age and fitness level, heart rates are grouped into different zones that people can target depending on training goals. A target heart rate during moderate-intensity activities is about 50-70% of maximum heart rate, while during vigorous physical activity it’s about 70-85% of maximum. We are going to define som training zones and see the hearth rate distribution by training intensity.

zone = [100, 125, 133, 142, 151, 173]
name = ['Easy', 'Moderate', 'Hard', 'Expert', 'God Like']
color = ['green', 'yellow', 'orange', 'tomato', 'red']
run_hr = run['2018':'2015 3']['Average Heart Rate (bpm)']

fig, ax = plt.subplots(figsize=(8, 5))
n, bins, patches = ax.hist(run_hr, bins=zone, alpha=0.5)
for i in range(0, len(patches)):
    patches[i].set_facecolor(color[i])
ax.set(title='Distribution of HR', ylabel='Number of runs')
ax.xaxis.set(ticks=zone[0:5])
ax.set_xticklabels(labels=name, rotation=-30, ha='left')

plt.show()

Summary

By this time we have already have visualizated and analyzed our workout performance, however a summary showing our totals would be a great resource. First, we want a summary of the distance (km) and climb (m) variables for each workout type. And second, we want the statistics for the average speed (km/hr), climb (m), and distance (km) variables for each workout type.

run_walk_cycle = df_run.append(walk).append(cycle).sort_index(ascending=False)
dist_climb, speed_col = ['Distance (km)', 'Climb (m)'], ['Average Speed (km/h)']
totals = run_walk_cycle.groupby('Type')[dist_climb].sum()

print('Training types totals:')
display(totals)

summary = run_walk_cycle.groupby('Type')[dist_climb + speed_col].describe()
for i in dist_climb:
    summary[i, 'total'] = totals[i]
print('Training types statistics summary:')
summary.stack()

Training types totals:

	Distance (km)	Climb (m)
Type
Cycling	680.58	6976
Running	5224.50	57278
Walking	33.45	349

Training types statistics summary:

		Average Speed (km/h)	Climb (m)	Distance (km)
Type
Cycling	25%	16.980000	139.000000	15.530000
	50%	19.500000	199.000000	20.300000
	75%	21.490000	318.000000	29.400000
	count	29.000000	29.000000	29.000000
	max	24.330000	553.000000	49.180000
	mean	19.125172	240.551724	23.468276
	min	11.380000	58.000000	11.410000
	std	3.257100	128.960289	9.451040
	total	NaN	6976.000000	680.580000
Running	25%	10.495000	54.000000	7.415000
	50%	10.980000	91.000000	10.810000
	75%	11.520000	171.000000	13.190000
	count	459.000000	459.000000	459.000000
	max	20.720000	982.000000	38.320000
	mean	11.056296	124.788671	11.382353
	min	5.770000	0.000000	0.760000
	std	0.953273	103.382177	4.937853
	total	NaN	57278.000000	5224.500000
Walking	25%	5.555000	7.000000	1.385000
	50%	5.970000	10.000000	1.485000
	75%	6.512500	15.500000	1.787500
	count	18.000000	18.000000	18.000000
	max	6.910000	112.000000	4.290000
	mean	5.549444	19.388889	1.858333
	min	1.040000	5.000000	1.220000
	std	1.459309	27.110100	0.880055
	total	NaN	349.000000	33.450000

Conclusions and Recommendations

It is important to engage in regular physical activity in order to maintain a healthy lifestyle however a large portion of the population is insufficiently active.

Keeping track of your workout data can help you to improve your performance and keep motivation. If you are a data enthusiast making a custom analysis of your data would be a useful and interesting way yo get some insights of your progress.