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Challenges in Haptic Communications Over the Tactile Internet

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Presentation on theme: "Challenges in Haptic Communications Over the Tactile Internet"— Presentation transcript:

1 Challenges in Haptic Communications Over the Tactile Internet
IEEE Access, 2017

2 What are haptic data? - Touch - Proprioception - Kinaesthesia

3 Haptic inputs are highly multidimensional.
pain, temperature motion texture skin sketch vibration Ref: Fundamental Neuroscience, 4th ed., 2013

4 Haptic inputs are highly multidimensional.
Tendon stretch (muscle contraction) Muscle stretch Ref: Fundamental Neuroscience, 4th ed., 2013

5 Haptic application #1: Teleoperation example
Ocean One (Stanford) Ref: Khatib O, Yeh X, Brantner G, Soe B, Kim B, Ganguly S, Stuart H, Wang S, Cutkosky M, Edsinger A, Mullins P. Ocean one: A robotic avatar for oceanic discovery. IEEE Robotics & Automation Magazine Dec;23(4):20-9.

6 Haptic application #2: Virtual environment example
PalpSim (training for needle insertion into the femoral artery ) Ref: Coles TR, John NW, Gould D, Caldwell DG. Integrating haptics with augmented reality in a femoral palpation and needle insertion training simulation. IEEE Transactions on Haptics Jul;4(3):

7 Haptic communications over the Internet

8 Haptic Internet Requirement #1: Very low End-to-End (E2E) delay
1 ms challenge?

9 Other Haptic Internet requirements
Reliability Transparency

10 Component-specific challenges

11 Component-specific challenges #1: Haptic sensors
Challenge: Transparency vs. Delay tradeoff Ref for figure on right:Wang Y, Liang G, Mei D, Zhu L, Chen Z. A flexible capacitive tactile sensor array with high scanning speed for distributed contact force measurements. InMicro Electro Mechanical Systems (MEMS), 2016 IEEE 29th International Conference on 2016 Jan 24 (pp ). IEEE.

12 Component-specific challenges #2: Haptic actuators
freely moving grounded vibrotactile kinaesthetic Challenge: More ergonomic options for force feedback during free movement. See also Figure 3.

13 Component-specific challenges #3: Data compression
Weber’s Law of Just Noticeable Differences (JND) I = stimulus intensity; k = constant ΔI : JND Deadbands in compression: Challenge: What if the stimulus train is highly volatile (or very low intensity)?

14 QoS and Software-Defined Networking (SDN)
Challenges to meeting hard QoS requirements: Current network algorithms adapt to changing network conditions slowly. Current QoS architectures (IntServ) are rarely used on the Internet, and not scalable.

15 QoS and Software-Defined Networking (SDN)
SDN: Separation of the control and data planes Ref: Sezer S, Scott-Hayward S, Chouhan PK, Fraser B, Lake D, Finnegan J, Viljoen N, Miller M, Rao N. Are we ready for SDN? Implementation challenges for software-defined networks. IEEE Communications Magazine Jul;51(7):36-43.

16 SDN: How is this helpful for meeting QoS requirements?
Reduced burden on network nodes (switches); SDN controllers manage QoS. SDN controllers benefit from centralized view of network (helpful for detecting rapidly changing network conditions) Remaining challenges: QoS routing has largely focused on bandwidth constraints. Relatively little work has considered SDN in carrier networks

17 QoS and 5G Source: Qualcomm, 2016

18 What if meeting the E2E requirement is impossible?
Prediction techniques: Simple: Linear extrapolation from most recently received samples. Complex: Predict with a model of the remote environment

19 Summary of take-homes:
There are lots of kinds of haptic data! Stringent QoS requirements are a current hurdle to the haptic Internet, but emerging technologies may help overcome this issue. Many challenges remain in developing and deploying effective haptic sensors, actuators, and compression algorithms. Prediction algorithms may help fill the delays in haptic feedback that are unavoidable.

20 Comments? Questions?


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