Heart Beat Rate calculation has traditionally been conducted using specialized hardware most commonly in the form of pulse oximeters or Electrocardiogram devices. Even though these methods offer high reliability, they require the users to have special sensor to measure their heart rate. In this paper we propose a system capable of estimating the heart beat rate using just a camera from a commercially available mobile phone. The advantage of this method is that the user does not need specialized hardware and s/he can take a measurement in virtually any place under almost any circumstances. Moreover the measurement provided can be used as a tool for health coaching applications or effective telecare services aimed in enhancing the user's well being.

Parkinson's is a neurodegenerative condition associated with several motor symptoms including tremors and slowness of movement. Freezing of gait (FOG); the sensation of one's feet being "glued" to the floor, is one of the most debilitating symptoms associated with advanced Parkinson's. FOG not only contributes to falls and related injuries, but also compromises quality of life as people often avoid engaging in functional daily activities both inside and outside the home. In the current study, we describe a novel system designed to detect FOG and falling in people with Parkinson's (PwP) as well as monitoring and improving their mobility using laser-based visual cues cast by an automated laser system. The system utilizes a RGB-D sensor based on Microsoft Kinect v2 and a laser casting system consisting of two servo motors and an Arduino microcontroller. This system was evaluated by 15 PwP with FOG. Here, we present details of the system along with a summary of feedback provided by PwP. Despite limitations regarding its outdoor use, feedback was very positive in terms of domestic usability and convenience, where 12/15 PwP showed interest in installing and using the system at their homes. Implications for Rehabilitation Providing an automatic and remotely manageable monitoring system for PwP gait analysis and fall detection. Providing an automatic, unobtrusive and dynamic visual cue system for PwP based on laser line projection. Gathering feedback from PwP about the practical usage of the implemented system through focus group events.

The Microsoft Kinect RGB-D sensor has been proven to be a reliable tool for gait analysis and rehabilitation purposes. Although it is accurate for detecting upper body part movements, even the second iteration of the Kinect sensor lacks the accuracy when it comes to lower extremities. while detecting foot-off and foot contact phases of a gait cycle is an important part of a gait performance analysis, The Kinect's intrinsic inaccuracies make it an unreliable tool to detect them accurately. We propose a new Kinect based technique for detecting foot-off and foot contact phases in a gait cycle that solely relies on a subject's knee joint relative angle. The system was tested on 11 healthy subjects walking in pre-defined pathways in 12 walking sessions while the Kinect v2 camera was placed at different heights ranging from 0.65 to 1.57 and angles ranging from 0 to 45 degrees to the ground. The algorithm's accuracy was also compared to another footstep detection method based on the subject's ankle joints height to the ground. The results showed 86.52% accuracy in detecting foot-off and foot contact events on average for both feet.

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Abstract-In this paper, two algorithms were tested on 11 healthy adults: one based on heuristic and another one on video tagging machine learning methods for automatic fall detection; both utilizing Microsoft Kinect v2. For our heuristic approach, we used skeletal data to detect falls based on a set of instructions and signal filtering methods. For the machine learning approach, we implemented a dataset utilizing the Adaptive Boosting Trigger (AdaBoostTrigger) algorithm via video tagging to enable fall detection. For each approach, each subject on average has performed six true positive and six false positive fall incidents in two different conditions: one with objects partially blocking the sensor's view and one with partial obstructed field of view. The accuracy of each approach has been compared against one another in different conditions. The result showed an average of 95.42 % accuracy in the heuristic approach and 88.33 % in machine learning technique. We conclude that heuristic approach performs more accurately for fall detection when there is a limited number of training dataset available. Nevertheless, as the gesture detection's complexity increases, the need for a machine learning technique is inevitable.