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ADL Assistant: An Aware Remote Caregiving System

ADL Assistant: An Aware Remote Caregiving System. Tom Keating Eugene Research Institute Ray Keating Pontimax Technologies, Inc. Remote Caregiving Support. Integrating home sensor networks, smart software and web-based tools to provide data for consumers and caregivers Applications:

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ADL Assistant: An Aware Remote Caregiving System

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  1. ADL Assistant: An Aware Remote Caregiving System Tom Keating Eugene Research Institute Ray Keating Pontimax Technologies, Inc.

  2. Remote Caregiving Support • Integrating home sensor networks, smart software and web-based tools to provide data for consumers and caregivers • Applications: - Providing long distance behavioral support - Helping avoid victimization - Monitoring physical health and communicating information to health care providers - Monitoring environmental changes related to health and safety

  3. Test Bed Example of Remote Monitoring in Practice Main Components • Picture Planner™ activity planning and prompting application • X10-based home sensor network • Home automation software • Consumer and caregiver monitoring computers • Intelligent activity recognition software • Webcams or IP Cameras

  4. Apartment Layout 7 PIR motion sensors 1 magnetic reed sensor CM15 powerline interface

  5. Remote Desktop View

  6. Bath Shower Bdr LR DR Porch Fr. dr. Web Portal for Caregiver Kitchen Summaries ______________________M_____T____W____Th____F____S____Su Showers 1 1 0 1 0 1 0 Sleep hours 6.5 12 7 8 8.5 8.5 6 Nighttime door 0 0 0 0 0 0 0 Nighttime bath use 2 1 1 1 2 3 1

  7. Benefits • Improved self-management for activity and task completion • Feasible universally cost effective remote support technology • Caregiver effectiveness increased • Caregiver peace of mind increased • Some functionality increasingly available “off the shelf”

  8. Smart Prompting Prototype: Planned Activity vs. Actual Behavior

  9. Conceptual Model for Behavioral Inferencing in Residential Settings

  10. Behavioral Inferencing for Activities of Daily Living The “Sensible” Spatio-Temporal Way

  11. Objective 1 Provide near real time oversight of consumer in their activities of daily living by: • Infer simple-to-complex ADL of interest from motion sensor data • Selectively generate multi modal, immediate (email, SMS texting) advisories to caregiver

  12. Objective 2 • Provide positive consumer ADL direction though: • Use of inferred events, activities & behaviors to drive generation of multi-media audio & video prompts • Communicate prompts to consumer at residence domain area

  13. Objective 3 • Use least obtrusive, least expensive (!) means possible to achieve Objectives 1 & 2

  14. Working Back From Objective 3 • Passive infrared (PIR) sensors can detect motion • Are inconspicuous • Are inexpensive ($15 - $30) • Can be easily installed at the consumer’s residence

  15. What’s a PIR Sensor? • Are area sensors • Fire on motion in their field of view (FOV) • Commonly used to turn on a light when the room is entered Can they meet the challenges posed by objectives 1 & 2?

  16. The Challenge is one of BOTH Sense & Sensibility • “Sensing” • Can the ADL be captured by motion sensors? • “Sensibility” • Do ADL have characteristic patterns in the form of spatio-temporal motion occurrences? • What would these ADL characteristics be based on?

  17. Can the ADL be Captured by Motion Sensors? • To answer the question… • Let’s take a look at how the ADL motion data generation and collection occurs using PIR type motion sensors

  18. Capturing the ADL Motion Data • Coverage • Consumer residence “domain” divided into “Domain Areas”: - Shower, bath, bedroom, living room, dining area, kitchen, front door, porch • One PIR sensor per domain area • Sensing Capabilities • Installed PIR sensors fire once every ten seconds for motions in FOV • Motions continuously detected for all domain areas

  19. Capturing the ADL Motion Data, Con’t. • Real time data collection • Consumer site unit receives transmitted sensor motion data • Time stamps location tagged motion data • Continuous uploading to database server OK, it seems that movements at locations (spatial) can be detected by motion sensors and chronologically (temporal) recorded in a database - How are these spatio-temporal motion patterns recognized as a particular ADL?

  20. Characterizing ADL with Spatio-Temporal Fact Patterns • Empirical studies indicate that many ADL can be characterized as: • A Certain number of motions • Within a time period • At a particular location Clearly, location (& maybe time) context is paramount! Example: Taking a daily shower • If more than X motions within Y duration at the SHOWER domain area, then consumer has taken their daily shower!

  21. How to Define the Spatio-Temporal Fact Patterns? • Might… • Caregiver experience… • Mathematical analysis… • Behavioral research… …Be used?

  22. How to Define the Spatio-Temporal Fact Patterns, con’t-2 • Example: • The Caregiver knows from experience (“A Posteriori”) that the Consumer generally takes five to ten minute showers. • The motion sensor detects movement, at most, once every ten seconds so in a five to ten minute period there should be a pattern of motion detections in the range of 50 to 100 occurrences, assuming constant movement in the shower. Let’s use some data analysis to add some Assurance to our caregiver’s experience…

  23. How to Define the Spatio-Temporal Fact Patterns, con’t-3 • Use Time-Location Based Clustering Analysis: - Do the data collection over some sufficient period -- say a week - Aggregate the sensor data occurrences for each domain area by time period, using the caregiver’s A Posteriori observation. • Results: The basic spatio-temporal “shower taken” pattern confirmed, albeit, at a lower occurrence rate of 30 to 60 occurrences over a five to ten minute duration.

  24. How do we Apply our ADL Fact Patterns to the Data? • Three requirements: • Describe the spatio-temporal fact patterns • Store them for access from the database • Use them to inference ADL occurrences

  25. Use Spatio-Temporal Predicate Expressions to Specify the ADL Patterns • General form: • @Pattern Meta Function [tag-value argument set]: • Spatio-temporal context (tag-value context reference:value set) • Predicate Operator (=,>=,<=, !=) • ! Pattern Meta Criterion [criterion argument(s)]: • Spatio-temporal context (tag-value context reference:value set) • Showering_Activity: @OCCURRENCES[SCANFREQ=’10’,DURATION=’10’]: CONTEXT(LOCATION=‘SHOWER‘, TIMESCOPE=‘2000-2200’ ) >= !OCCURRENCES[CRITERION_OCCURRENCES,CRITERION_DURATION]: CONTEXT(LOCATION=‘SHOWER', TIMEFRAME=‘0000-2400’)

  26. CAUTION: • Many ADL patterns can’t be reliably inferred by just a single motion pattern • Can the Level of “Sensibility” be raised? • Sure, just specify additional inferencing patterns to form a predicate inferencing chain: • If fact pattern-1, then if fact pattern-N… ADL of interest occurred

  27. Spatio-Temporal Fact Patterns • For Shower taken, for example, how can it be assured that the consumer didn’t just step in and out of the shower? Simple – define a spatio-temporal fact pattern to establish that the consumer was continuously in the shower… • CONTINUOUS_PRESENCE-SHOWER: @DURATION[SPAN='CONTINUOUS']:CONTEXT(LOCATION= ‘SHOWER',TENSE='CURRENT') >= !DURATION[ACTUAL_DURATION]:CONTEXT (LOCATION=‘SHOWER',TEMPORAL='CURRENT')

  28. Inference Chaining of ADL Predicate Patterns • A “shower taken” is now defined as: Shower_Taken Showering -activity Continuous-Presence-Shower • Chained Inferencing Predicate Description • If the sensor data pattern described by Showering_activity occurred AND if at that same time CONTINUOUS_PRESENCE-SHOWER were true, then it is very likely that a shower was taken by the consumer!

  29. Behavioral Plans • Inferencing hierarchies of ADL concepts can be defined to for a “Plan” • The ADL concepts provided are: Plans - Made up of behaviors Behaviors - Made up of activity patterns Activity patterns - Made up of activities Activities - Made up of events Events - Made up of inference fact patterns • Currently goal times can be specified for each concept entity. • Soon, goal expressions consisting of inference fact patterns will be specifiable for all behavior plan concepts.

  30. General and Predicate Events • Can be defined as stand-alone entities • Consist of inference fact patterns • Can be “scored” or not - scored have goal times and have non-occurrence as well as occurrence inferencing performed • SHOWER_TAKEN is a general event • Location context maintenance accomplished by a set of general events • Predicate events can be defined to be inferenced upon occurrence of a specified triggering event.

  31. Consequent Actions • Can be specified for any “inferencing entity” • Behavior plan concepts: inferred events, activities, etc. • General and predicate events • Performed on the occurrence of the inferred entity • Can be specified to be performed on positive occurrence, negative occurrence of ALL • Currently email event occurrence reports, cell phone (SMS) texting and text-to-speech prompt generation • Can be specified as “external action” consequent actions.

  32. The Inferencing Agent uses the Pattern Meta Specification to do the Inferencing • Retrieves the stored behavior plans, general & predicate events, and inference fact pattern meta specifications • Generates the necessary data base queries and inferencing rules and fact patterns • Performs the Inferencing per the fact pattern’s datescope and timescope, at the scan frequency specified • Accomplishes any specified consequent actions

  33. Questions? • tkeating@eugeneresearch.org • rayk@pontimax.com

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