Skip to main content

Activity Recognition

Project description

DataSet

Dataset Format:

Object:

An object is a primitive object, a vector or in the form of a tuple of data components:
Object ={o|     o is Primitive or
                o=[o_1, ... , o_n] such that o_i is Object(Vector of object) or
                o=(Prop_1, ... , Prop_n) forall i in {1...n}, Prop_i(o) is Object}

Time Object:

Time might be a point, in case of an instantaneous event, or an interval during if it is durative. Supported durative time is range.

time | [start_time:end_time]

Event:

Type Actor Time

Sensor Events:

(Type, Value) SensorId Time

Activity Events:

ActivityId ActorId Time

DataInformation:

Sensor Info

Id Name Cumulative OnChange Nominal Range Location Object Sensor

Activity Info

Id Name

File format: CSV

Sensor Info:

Id Name Cumulative OnChange Nominal Range Location Object Sensor
int string bool bool bool json {min,max}/{items} string string string
in case of nominal sensors, the range contain items and for numeric sensors, the range contain min and max

Sensor events:

Type Value SensorId Time

Activity events:

ActivityId ActorId StartTime EndTime

Approaches

\begin{Example}[Different Segmentation approaches] \end{Example} \begin{lstlisting}[mathescape=true] function Fixed time window(S,X,r,l) {//S=SegmentHistory, X=Events, //r=Shift, l=windowLength p=begin(S[last]) return X.eventsIn([p + r : p + r + l]); } function Fixed siding window(S,X,r,l) { prev_w=S[last]; p=begin(S[last]) be=first({e \in X| p + r $\leq$ time(e)} return X.eventsIn([be : be + l]); } function Significant events(S,X,m) {//m=significant events per segments se=significantEvents(X) $\subseteq$ X begin=time(se[1]);//next significant event end=time(se[1 + m]); return X.eventsIn([begin:end]); } //Probabilistic Approach given:(By analyzing training set) $ws(A_m)$ is average window size of activity $A_m$ $w_1 = min {ws(A_1), ws(A_2), ..., ws(A_M)}$ $w_L = median{ws(A_1), ws(A_2), ..., ws(A_M)}$ $w_l=(w_L-w_1)\times l/L+w_1$ $window_sizes= {w_1, w_2, . . . , w_L}$ $P(w_l /A_m)$//probability of windows length $w_l$ for an activity Am $P(A_m /s_i)$//probability of Activity $A_i$ associated with the sensor $s_i$. function Probabilistic Approach(S,X) { x=nextEvent(X) $w^{\star} =\underset{w_l}{max} {P(w_l /x)}=\underset{w_l}{max}[P(w_l /A_m)\times P(A_m /x)] $ end=time(x);//Next event return X.eventsIn(end-$w^\star$,end]); } function Metric base Approach(S,X) {//S=SegmentHistory, X=Events
indx=len(S[last])+1 //first event not in old segment $m_i=metric({X[indx],...,X[i]})$ find first i which $H({m_{0}....m_i})$ is true// return X.eventsIn([time(X[indx]):time(X[i])]); } function SWAB Approach(S,X,bs) {//bs=Buffer size
indx=len(S[last])+1 //first event not in old segment $m=BottomUp({X[indx],...,X[indx+bs]})$ return m[0]; } \end{lstlisting}

Similar Works

pyActLearn -> documents

Project details


Release history Release notifications

This version

1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for UnifiedAR, version 1
Filename, size File type Python version Upload date Hashes
Filename, size UnifiedAR-1.tar.gz (50.6 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page