def trainingDataFrom_walk1():
trainingSet = pd.DataFrame()
# In[39]:
dfO = pd.read_csv(pathToTrainingData + 'walk1.csv')
eventWindow = clip(dfO, 7.4, 8.7)
label = eventsDict['walk'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 8.7, 9.2)
label = eventsDict['walk'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 9.2, 9.9)
label = eventsDict['walk'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 9.9, 10.5)
label = eventsDict['walk'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def testDataFrom_ME1():
trainingSet = pd.DataFrame()
# In[39]:
dfO = pd.read_csv(pathToTestData + 'multEvents1.csv')
eventWindow = clip(dfO, 6, 7)
label = eventsDict['rest'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 8, 10)
label = eventsDict['walk'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 25, 28)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 31, 57)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 71, 74)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 88, 90)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 92, 96)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 101, 104)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 109, 112)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 115, 119)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def trainingDataFrom_type1():
trainingSet = pd.DataFrame()
# In[39]:
dfO = pd.read_csv(pathToTrainingData + 'type1.csv')
eventWindow = clip(dfO, 5.0, 6.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 6.0, 7.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 7.0, 8.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 8.0, 9.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 21.0, 22.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 24.0, 25.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 26.0, 27.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(dfO, 28.0, 29.0)
label = eventsDict['type'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def activity_recognizer(fileName, startTime, endTime):
accl_readings = pd.read_csv(fileName)
accl_readings = clip(accl_readings, startTime, endTime)
eventSequence = find_events(accl_readings)
activity_fsm = FSM(superStates, internalStatesBySS, SSTransitions,
internalTransitionsBySS)
# get initial state of the FSM. the FSM will remain in this state until the
# first event occurs.
(initSSName, initIntSName) = activity_fsm.currentStateLabels()
activitySequence = [(eventsDict["unk"][0], 0, eventSequence[0][1],
initSSName, initIntSName)]
# print(eventSequence)
for event in eventSequence:
activity_fsm.transition(event[0])
(SSName, intSName) = activity_fsm.currentStateLabels()
# print(event[0])
# print(SSName, intSName)
activitySequence += [(event[0], event[1], event[2], SSName, intSName)]
return activitySequence
def trainingDataFrom_standtostand3():
# In[0]:
df = pd.read_csv(pathToTrainingData + 'standtostand3.csv')
trainingSet = pd.DataFrame()
# In[1]:
eventWindow = clip(df, 11, 15)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[2]:
eventWindow = clip(df, 16, 21)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[3]:
eventWindow = clip(df, 26, 31)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[4]:
eventWindow = clip(df, 34, 38)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[5]:
eventWindow = clip(df, 44, 48)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[6]:
eventWindow = clip(df, 60, 65)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[7]:
eventWindow = clip(df, 68, 70)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def trainingDataFrom_standtostand2():
# In[0]:
df = pd.read_csv(pathToTrainingData + 'standtostand2.csv')
trainingSet = pd.DataFrame()
# In[1]:
eventWindow = clip(df, 18, 21)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[2]:
eventWindow = clip(df, 23, 27)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[3]:
eventWindow = clip(df, 33, 37)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[4]:
eventWindow = clip(df, 42, 46)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[5]:
eventWindow = clip(df, 55, 58)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[6]:
eventWindow = clip(df, 66, 70)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[7]:
eventWindow = clip(df, 77, 81)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[8]:
eventWindow = clip(df, 87, 90)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def trainingDataFrom_sitstandmult():
# In[0]:
df = pd.read_csv(pathToTrainingData+'sitstandmult.csv')
trainingSet = pd.DataFrame()
# In[1]:
eventWindow = clip(df, 9, 12)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 12, 16)
# label = eventsDict['lieDown'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 19, 23)
# label = eventsDict['sitUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 24, 26)
# label = eventsDict['standUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(df, 29, 32)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 33, 36)
# label = eventsDict['lieDown'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 37, 41)
# label = eventsDict['sitUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 43, 45)
# label = eventsDict['standUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(df, 47, 50)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 50, 54)
# label = eventsDict['lieDown'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 56, 59)
# label = eventsDict['sitUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 60, 62)
# label = eventsDict['standUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(df, 65, 68)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 68, 72)
# label = eventsDict['lieDown'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 73, 77)
# label = eventsDict['sitUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
# eventWindow = clip(df, 78, 80)
# label = eventsDict['standUp'][1]
# trainingInstance = convertRawToTrainingInstance(eventWindow, label, reverseEventsDict[label])
# trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def trainingDataFrom_standtostand1():
# In[0]:
df = pd.read_csv(pathToTrainingData + 'standtostand1.csv')
trainingSet = pd.DataFrame()
# In[2]:
eventWindow = clip(df, 8.5, 12)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[3]:
eventWindow = clip(df, 16, 20)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[4]:
eventWindow = clip(df, 28, 32)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[5]:
eventWindow = clip(df, 34, 37)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[6]:
eventWindow = clip(df, 45, 49)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[7]:
eventWindow = clip(df, 51, 55)
label = eventsDict['turn'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[8]:
eventWindow = clip(df, 60, 64)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
eventWindow = clip(df, 68, 70)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet
def trainingDataFrom_one():
trainingSet = pd.DataFrame()
# In[39]:
one = pd.read_csv(pathToTrainingData + 'one.csv')
one_1 = clip(one, 5, 50)
# First sit down event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_1, 12, 15)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# First doing nothing event. Add features to training set.
# -----------------------------------------------------------
eventWindow = clip(one_1, 16, 20)
label = eventsDict['rest'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# First lyingDown activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_1, 21, 25)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# 1st situp event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_1, 35, 41)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Second doing nothing event. Add features to training set.
# -----------------------------------------------------------
eventWindow = clip(one_1, 42, 45)
label = eventsDict['rest'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# First stand up activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_1, 46, 48)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[40]:
one_2 = clip(one, 51, 100)
# Next sit down event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_2, 57, 60)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Another doing nothing event. Add features to training set.
# -----------------------------------------------------------
eventWindow = clip(one_2, 61, 64)
label = eventsDict['rest'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# First lyingDown activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_2, 64, 69)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# 2nd situp event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_2, 84, 89)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Second stand up activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_2, 94, 98)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[41]:
one_3 = clip(one, 140, 190)
# Next sit down event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_3, 146, 149)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Another doing nothing event. Add features to training set.
# -----------------------------------------------------------
eventWindow = clip(one_3, 151, 155)
label = eventsDict['rest'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# First lyingDown activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_3, 155, 159)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# 2nd situp event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_3, 170, 176)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Second stand up activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_3, 180, 183)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# In[42]:
one_4 = clip(one, 190, 235)
# Next sit down event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_4, 191, 195)
label = eventsDict['sitDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Another doing nothing event. Add features to training set.
# -----------------------------------------------------------
eventWindow = clip(one_4, 204, 210)
label = eventsDict['rest'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# First lyingDown activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_4, 198, 203)
label = eventsDict['lieDown'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# 2nd situp event. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_4, 218, 223)
label = eventsDict['sitUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
# Second stand up activity. Add features to training set.
# ------------------------------------------------------
eventWindow = clip(one_4, 228, 232)
label = eventsDict['standUp'][1]
trainingInstance = convertRawToTrainingInstance(eventWindow, label,
reverseEventsDict[label])
trainingSet = trainingSet.append(trainingInstance)
return trainingSet