Ramin Irani

Improved pulse detection from head motions using DCT

The heart pulsation sends out the blood throughout the body. The rate in which the heart performs this vital task, heartbeat rate, is of curial importance to the body. Therefore, measuring heartbeat rate, a.k.a. pulse detection, is very important in many applications, especially the medical ones. To measure it, physicians traditionally, either sense the pulsations of some blood vessels or install some sensors on the body. In either case, there is a need for a physical contact between the sensor and the body to obtain the heartbeat rate. This might not be always feasible, for example, for applications like remote patient monitoring. In such cases, contactless sensors, mostly based on computer vision techniques, are emerging as interesting alternatives. This paper proposes such a system, in which the heartbeats (pulses) are detected by subtle motions that appear on the face due to blood circulation. The proposed system has been tested in different facial expressions. The experimental results show that the proposed system is correct and robust and outperforms state-of-the-art.

Pain recognition using spatiotemporal oriented energy of facial muscles

Pain is a critical sign in many medical situations and its automatic detection and recognition using computer vision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the proposed system in this paper employs steerable and separable filters to measures energies released by the facial muscles during the pain process. The proposed system not only detects the pain but recognizes its level. Experimental results on the publicly available pain database of UNBC show promising outcome for automatic pain detection and recognition.

Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition

Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal facial images for pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames, improving by more than 6% the results that only consider RGB data.

Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity

Physical fatigue reveals the health condition of a person at, for example, health checkup, fitness assessment, or rehabilitation training. This study presents an efficient non-contact system for detecting non-localised physical fatigue from maximal muscle activity using facial videos acquired in a realistic environment with natural lighting where subjects were allowed to voluntarily move their head, change their facial expression, and vary their pose. The proposed method utilises a facial feature point tracking method by combining a ‘good feature to track’ and a ‘supervised descent method’ to address the challenges that originate from realistic scenario. A face quality assessment system was also incorporated in the proposed system to reduce erroneous results by discarding low quality faces that occurred in a video sequence due to problems in realistic lighting, head motion, and pose variation. Experimental results show that the proposed system outperforms video-based existing system for physical fatigue detection.

Validation and Test of a Closed-Loop Tele-rehabilitation System Based on Functional Electrical Stimulation and Computer Vision for Analysing Facial Expressions in Stroke Patients

The aim of the present study was to validate and test a closed-loop tele-rehabilitation system for training of hand function and analyzing facial expressions in stroke patients. The paper presents the methods for controlling functional electrical stimulation (FES) to assist hand opening and grasping. The main outcome of the FES control was time differences in grip detections performed by the automatic system and by analysis of the output from force sensing resistors. This time difference was in the range of 0 to 0.8 s. Results from analysis of facial expressions were very variable showing that subjects were disgusted, happy and angry during the exercises, which were not in agreement with the observations made during the experimental sessions.

Contactless measurement of muscles fatigue by tracking facial feature points in a video

Physical exercise may result in muscle tiredness which is known as muscle fatigue. This occurs when the muscles cannot exert normal force, or when more than normal effort is required. Fatigue is a vital sign, for example, for therapists to assess their patients progress or to change their exercises when the level of the fatigue might be dangerous for the patients. The current technology for measuring tiredness, like Electromyography (EMG), requires installing some sensors on the body. In some applications, like remote patient monitoring, this however might not be possible. To deal with such cases, in this paper we present a contactless method based on computer vision techniques to measure tiredness by detecting, tracking, and analyzing some facial feature points during the exercise. Experimental results on several test subjects and comparing them against ground truth data show that the proposed system can properly find the temporal point of tiredness of the muscles when the test subjects are doing physical exercises.

Contact-Free Heartbeat Signal for Human Identification and Forensics

The heartbeat signal, which is one of the physiological signals, is of great importance in many real-world applications, for example, in patient monitoring and biometric recognition. The traditional methods for measuring such this signal use contact-based sensors that need to be installed on the subject’s body. Though it might be possible to use touch-based sensors in applications like patient monitoring, it will not be that easy to use them in identification and forensics applications, especially if subjects are not cooperative. To deal with this problem, recently computer vision techniques have been developed for contact-free extraction of the heartbeat signal. We have recently used the contact-free measured heartbeat signal, for biometric recognition, and have obtained promising results, indicating the importance of these signals for biometrics recognition and also for forensics applications. The importance of heartbeat signal, its contact-based and contact-free extraction methods, and the results of its employment for identification purposes, including our very recent achievements, are reviewed in this chapter.

Thermal super-pixels for bimodal stress recognition

Stress is a response to time pressure or negative environmental conditions. If its stimulus iterates or stays for a long time, it affects health conditions. Thus, stress recognition is an important issue. Traditional systems for this purpose are mostly contact-based, i.e., they require a sensor to be in touch with the body which is not always practical. Contact-free monitoring of the stress by a camera [1], [2] can be an alternative. These systems usually utilize only an RGB or a thermal camera to recognize stress. To the best of our knowledge, the only work on fusion of these two modalities for stress recognition is [3] which uses a feature level fusion of the two modalities. The features in [3] are extracted directly from pixel values. In this paper we show that extracting the features from super-pixels, followed by decision level fusion results in a system outperforming [3]. The experimental results on ANUstressDB database show that our system achieves 89% classification accuracy.