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Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.
Cristofer Englund; Eren Aksoy; Fernando Alonso-Fernandez; Martin Cooney; Sepideh Pashami; Björn Åstrand. AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities 2021, 4, 783 -802.
AMA StyleCristofer Englund, Eren Aksoy, Fernando Alonso-Fernandez, Martin Cooney, Sepideh Pashami, Björn Åstrand. AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities. 2021; 4 (2):783-802.
Chicago/Turabian StyleCristofer Englund; Eren Aksoy; Fernando Alonso-Fernandez; Martin Cooney; Sepideh Pashami; Björn Åstrand. 2021. "AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control." Smart Cities 4, no. 2: 783-802.
Rehabilitation and mobility training of post-stroke patients is crucial for their functional recovery. While traditional methods can still help patients, new rehabilitation and mobility training methods are necessary to facilitate better recovery at lower costs. In this work, our objective was to design and develop a rehabilitation training system targeting the functional recovery of post-stroke users with high efficiency. To accomplish this goal, we applied a bilateral training method, which proved to be effective in enhancing motor recovery using tactile feedback for the training. One participant with hemiparesis underwent six weeks of training. Two protocols, “contralateral arm matching” and “both arms moving together”, were carried out by the participant. Each of the protocols consisted of “shoulder abduction” and “shoulder flexion” at angles close to 30 and 60 degrees. The participant carried out 15 repetitions at each angle for each task. For example, in the “contralateral arm matching” protocol, the unaffected arm of the participant was set to an angle close to 30 degrees. He was then requested to keep the unaffected arm at the specified angle while trying to match the position with the affected arm. Whenever the two arms matched, a vibration was given on both brachialis muscles. For the “both arms moving together” protocol, the two arms were first set approximately to an angle of either 30 or 60 degrees. The participant was asked to return both arms to a relaxed position before moving both arms back to the remembered specified angle. The arm that was slower in moving to the specified angle received a vibration. We performed clinical assessments before, midway through, and after the training period using a Fugl-Meyer assessment (FMA), a Wolf motor function test (WMFT), and a proprioceptive assessment. For the assessments, two ipsilateral and contralateral arm matching tasks, each consisting of three movements (shoulder abduction, shoulder flexion, and elbow flexion), were used. Movements were performed at two angles, 30 and 60 degrees. For both tasks, the same procedure was used. For example, in the case of the ipsilateral arm matching task, an experimenter positioned the affected arm of the participant at 30 degrees of shoulder abduction. The participant was requested to keep the arm in that position for ~5 s before returning to a relaxed initial position. Then, after another ~5-s delay, the participant moved the affected arm back to the remembered position. An experimenter measured this shoulder abduction angle manually using a goniometer. The same procedure was repeated for the 60 degree angle and for the other two movements. We applied a low-cost Kinect to extract the participant’s body joint position data. Tactile feedback was given based on the arm position detected by the Kinect sensor. By using a Kinect sensor, we demonstrated the feasibility of the system for the training of a post-stroke user. The proposed system can further be employed for self-training of patients at home. The results of the FMA, WMFT, and goniometer angle measurements showed improvements in several tasks, suggesting a positive effect of the training system and its feasibility for further application for stroke survivors’ rehabilitation.
Abbas Orand; Eren Erdal Aksoy; Hiroyuki Miyasaka; Carolyn Weeks Levy; Xin Zhang; Carlo Menon. Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM. Sensors 2019, 19, 3474 .
AMA StyleAbbas Orand, Eren Erdal Aksoy, Hiroyuki Miyasaka, Carolyn Weeks Levy, Xin Zhang, Carlo Menon. Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM. Sensors. 2019; 19 (16):3474.
Chicago/Turabian StyleAbbas Orand; Eren Erdal Aksoy; Hiroyuki Miyasaka; Carolyn Weeks Levy; Xin Zhang; Carlo Menon. 2019. "Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM." Sensors 19, no. 16: 3474.
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory that facilitates encoding, recalling, and predicting action experiences. Our proposed unsupervised deep episodic memory model as follows: First, encodes observed actions in a latent vector space and, based on this latent encoding, second, infers most similar episodes previously experienced, third, reconstructs original episodes, and finally, predicts future frames in an end-to-end fashion. Results show that conceptually similar actions are mapped into the same region of the latent vector space. Based on these results, we introduce an action matching and retrieval mechanism, benchmark its performance on two large-scale action datasets, 20BN-something-something and ActivityNet and evaluate its generalization capability in a real-world scenario on a humanoid robot.
Jonas Rothfuss; Fabio Ferreira; Eren Erdal Aksoy; You Zhou; Tamim Asfour. Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution. IEEE Robotics and Automation Letters 2018, 3, 4007 -4014.
AMA StyleJonas Rothfuss, Fabio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour. Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution. IEEE Robotics and Automation Letters. 2018; 3 (4):4007-4014.
Chicago/Turabian StyleJonas Rothfuss; Fabio Ferreira; Eren Erdal Aksoy; You Zhou; Tamim Asfour. 2018. "Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution." IEEE Robotics and Automation Letters 3, no. 4: 4007-4014.