Using Temporal Logic and Model Checking in Automated Recognition of Human Activities for Ambient- Assisted Living Authors : Tommaso Magherini, Alessandro Fantechi, Chris D. Nugent, and Enrico Vicario IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, VOL. 43, NO. 6, NOVEMBER 2013 Reporter : Nien Fu Hsiao 2015/12/05

Outline  Introduction  Application Scenario  System Overview  Experiment  Conclusion 2

Introduction  Increasing number of both the elderly and people suffering from long-term chronic conditions  Automated activity recognition  supporting the quality of life  improving living experience  Pervasive technologies  wireless sensor networks(WSN)  wired sensor networks  radio-frequency identification(RFID)  camera-based systems 3

Introduction  Novel description-based approach  formulae of propositional real-time TL (temporal logic)  events detected within a smart environment  Automated Recognizer of ADLs (ARA) 4

Application Scenario 5 Alice Prepare a coffee Smart Home Supervise Offer prompts

ADL Recognition Problem  Require a more concrete and detailed specification of ADLs  Dressing  taking clothes from a wardrobe  putting on overcoats  Distinguish three concepts at different levels:  Activity  Action  Observation 6

ADL Recognition Problem  Activity  Task undertaken by the observed subject with an intended goal that may be achieved through a sequence of different steps  Action  A single step possibly characterized by quantitative timing constraints that may occur in different activities  Relevant  Mandatory, Optional  Noisy  Observation  A measurement taken by some device that may occur during the execution of different steps 7

Temporal Logic for ADL  Propositional TL  Past oriented  Linear time  Discrete time  Real-time constraints  Bounded history  Real-time past linear Temporal Logic (R-pTL) 8

R-pTL  Yesterday (Y), Once (O), Historically (H), and Since (S)  c, d ∈ Z−, with c ≤ d  ∧, ∨, and ¬ 9

ARA System 10

ARA System 11

Experiment  Coffee preparation  Telephone usage  Take medication 12

Experiment  Coffee preparation  Correct preparation of a coffee  Incorrect coffee preparation activity  Water pouring into the cup (cpw)  Addition of the ground coffee (cpc)  Maximum time to prepare the coffee (tce)  The delay time (tcd) 13

Experiment  Correct  (O [−tce, 0] cpw) ∧ (O [−tce, 0] cpc)  Incorrect  ((H [ −tcd, −tcd] cpc) ∧ ( ¬ (O [ −tce, 0] cpw))) ∨  ((H [ −tcd, −tcd] cpw) ∧ ( ¬ (O [ −tce, 0] cpc))) 14

Experiment  Six activities addressed in the experimentation 15

Experiment in Smart Environment  DANTE Activity Monitoring System  DANTE marker  Two cameras  Dataset Creation 16

Experiment in Smart Environment  Three subjects  Mandatory actions  Nonmandatory actions  Noisy actions  Randomly selected subset of the activities 12 times 17

Experiment results  True positives (TP)  False positives (FP)  False negatives (FN) 18

Experiment results 19

Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer Authors : Piyush Gupta and Tim Dallas IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 6, JUNE 2014 Reporter : Nien-Fu Hsiao 2015/12/05

Outline  Introduction  System Components and Overview  Feature Selection  Activity Recognition  Conclusions And Future Work 21

Introduction  Falls are a major problem associated with old age  It is important to develop a technology  monitor gait of elderly people  This paper is focused on  Minimum number of sensors  Minimum number of analyzing data  Transitional events in ADLs  Independent of the accelerometer position around the waist 22

System Components and Overview 23

System Components and Overview  Belt-Clip Accelerometer  AT&T Gateway  MATLAB 24

Feature Selection 25

Feature Selection  Six activities  Walking  Jumping  Running  Sit-to-stand/stand-to-sit  Stand-to-kneel-to-stand  Stationary (sitting or standing at one place) 26

Feature Selection  Data signals  Segmented into windows of 6 s  50% overlap 27

Feature Selection 28

Feature Selection  Mean Trend and Windowed Mean Difference  6 s long acceleration series is further divided into 12 windows(0.5s each) 29

Feature Selection  Variance Trend and Windowed Variance Difference  6 s long acceleration series is further divided into 12 windows(0.5s each) 30

Feature Selection  DFA Coefficient  alpha (α)  0.5  <0.5  >0.5 31

Feature Selection  X−Z Energy Uncorrelated  Cross-correlation factor 32

Feature Selection  Maximum Difference Acceleration  Difference between maximum and minimum acceleration experienced on each axis 33

Feature Selection  Feature Selection  Relief-F  SFFS (sequential forward floating search)  Classifier  K-NN( k=10)  Naive Bayes 34

Feature Selection 35

Activity Recognition  Experimental Setup  Seven young healthy subjects  22 and 28 years of age with no walking impairment  Wear the triaxial accelerometer at their waist 36

Results 37

Results 38

Results 39

Conclusion  First paper  The ARA correctly recognizes the 85.4%  ARA offers a formal ground to reduce the semantic gap between raw data and high-level concepts  Recognition of human activities and behaviors still present several challenges 40

Conclusions  Second paper  System can classify different activities with high accuracy  Robust system  Minimum training of the users  Provide least errors due to orientation and positioning offsets 41