Human environments are typified by walls, homes, offices, schools, museums, hospitals and pretty much every indoor context one can imagine has walls. In many cases, they make up a majority of readily accessible indoor surface area, and yet they are static their primary function is to be a wall, separating spaces and hiding infrastructure. We present Wall++, a low-cost sensing approach that allows walls to become a smart infrastructure. Instead of merely separating spaces, walls can now enhance rooms with sensing and interactivity. Our wall treatment and sensing hardware can track users' touch and gestures, as well as estimate body pose if they are close. By capturing airborne electromagnetic noise, we can also detect what appliances are active and where they are located. Through a series of evaluations, we demonstrate Wall++ can enable robust room-scale interactive and context-aware applications.

Wall++: Room-Scale Interactive and Context-Aware Sensing

Physiological signals such as heart rate can provide valuable information about an individuals state and activity. However, existing work on computer vision has not yet explored leveraging these signals to enhance egocentric video understanding. In this work, we propose a model for reasoning on multimodal data to jointly predict activities and energy expenditures. We use heart rate signals as privileged self-supervision to derive energy expenditure in a training stage. A multitask objective is used to jointly optimize the two tasks. Additionally, we introduce a dataset that contains 31 hours of egocentric video augmented with heart rate and acceleration signals. This study can lead to new applications such as a visual calorie counter.

/pdf/jointly-learning-energy-expenditures-and-activities-using-42nr1ep6o7.pdf

Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals

Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambient-assisted living, and health monitoring and management Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components The state of the art in human activity recognition based on accelerometer is surveyed According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline) Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors

/pdf/survey-on-human-activity-recognition-based-on-acceleration-56hbzt9ahc.pdf

Survey on Human Activity Recognition based on Acceleration Data

https://research-information.bris.ac.uk/files/69167368/Classification_and_Suitability_of_Sensing_Tech_submitted.pdf

Classification and suitability of sensing technologies for activity recognition

We present a hierarchical framework for zero-shot human-activity recognition that recognizes unseen activities by the combinations of preliminarily learned basic actions and involved objects. The presented framework consists of gaze-guided object recognition module, myo-armband based action recognition module, and the activity recognition module, which combines results from both action and object module to detect complex activities. Both object and action recognition modules are based on deep neural network. Unlike conventional models, the proposed framework does not need retraining for recognition of an unseen activity, if the activity can be represented by a combination of the predefined basic actions and objects. This framework brings competitive advantage to industry in terms of the service-deployment cost. The experimental results showed that the proposed model could recognize three types of activities with precision of 77% and recall rate of 82%, which is comparable to a baseline method based on supervised learning.

Hierarchical Model for Zero-shot Activity Recognition using Wearable Sensors

Recent development of wearable technology has opened great opportunities for human performance evaluation applications in various domains. In order to measure the physical activities of an individual, wrist-worn sensors embedded in smart-watches, fitness bands, and clip-on devices can be used to collect various types of data while the subject performs regular daily activities.In this paper we are going to explain how to achieve activities of daily living(ADLs) using a sensor device attached to a users wrist. This device contains a camera, a microphone and an accelerometer. In this experience we will collect the data from the sensors in our device and try to analyse it, in order to recognise the type of the activity. In this way we will be able to recognize ADLs that contain manual use of objects such as making a drink or cooking. Finally, we will be able to say, that the camera plays the major rule in this experience and without it would be difficult to achieve our goal. We will also suggest a method that will protect the privacy of the user, as the camera and the microphone can records part of the users private life.

/pdf/object-based-activity-recognition-with-heterogeneous-sensors-31c0h6cyrh.pdf

Mohammad Albaida

Papers

Object-based Activity Recognition with Heterogeneous Sensors on Wrist