Table 2.

Recent work with sensor-based and vision-based inputs

Ref.	Year	Key contributions	Input type	Advantages	Disadvantages	Dataset used	Technique
[32]	2021	comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors	Sensor, Vision	Captures transitioning of activities too. Like walking to falling	Spectrograms may differ in most realistic scenarios as the human motions might not be restricted to a single aspect angle with respect to the radar. It could be due to the shadowing of some part of the human body if captured at a different angle	Gathered during experiment	ML techniques
[28]	2021	integrated system prototype that provides an efficient technological tool to caregivers operating promptly and ensures efficient performance throughout the entire healthcare system process	Sensor	Takes care of situations where the elderly are to be monitored every time without the use of expensive cameras etc	Sensor placements	Wireless Sensor Data Mining (WISDN) dataset in Jennifer R. Kwapisz et al. t. [153].	Localization using CNN
[214]	2021	Joint tracking and activity recognition in indoor environment using radar sensors	Radar sensors	Placement of sensors even in sensitive areas is not a privacy concern	Boundary error Data representation Imbalanced dataset Loss function	Collect own dataset using the setup uses K-fold method as dataset is limited	Deep learning
[146]	2021	(HAR) based on Radio Frequency energy harvesting (RFEH) as the harvested voltage signals of different human activities exhibit distinctive patterns.	Radio frequency	Accuracy and computational efficiency	Interference from other wifi devices Complex activities and variety of ways in which one may perform them.	Gathered dataset performance of the system by applying the four light-weight classifiers and calculating their accuracies on the collected datasets	Machine learning classifiers
[51]	2021	HAR for indoor using channel information of wifi signals	Wifi signals	Cost-effective as wifi devices are mostly already installed.	Interference in signal	Collected own data set	CNN
[182]	2021	Smart-home, smart-gym fitness tracker solution using a single mm-wave radar point cloud data	Radar sensors	The privacy of the user is intact. contactless, accurate, real-time fitness tracker system for indoor fitness activities, capable of running on edge devices for applications in IoT-connected healthcare	Interference/noise has not been commented upon	Data set collected from radar sensors	Deep learning
[170]	2021	Combination of acceleration audio and wifi round trip time localization to recognize indoor activities.	Wifi round trip audio acceleration	Audio related to activity gives another useful feature in recognition. Accuracy is consistent upon 12 activities	Limited activities, Maybe not suitable if activities with similar audio are tested, technology used is relatively new and hardware is not ubiquitous	Dataset created and model trained again and again for new activities	ML
[7]	2021	Gives a mathematical representation for human body as clusters of different points	Radio frequency	Time variant features and properties explored while representing human as cluster of scattered points	Signa based, stationary objects	CSI and IMU data were used as collected	ML
[83]	2015	online activity recognition system, which explores WiFi ambient signals for received signal strength indicator (RSSI) fingerprint of different activities.	Wifi signal data	Simple use. Fine average accuracy	context between persons and motions, context between locations and motions, and even context between emotions and motions can be explored as the current scenario is simple	Data collected using set up/ wifi	ML
[186]	2018	Uses video camera and radar sensors fusion for predicting activities	Radar sensors, video camera	Fusion of radar and video with deep learning gives good accuracy	Computation can be worked upon	Accuracy checked on Standard data set	DL
[148]	2019	robust approach for human activity recognition which uses the open source library OpenPose to extract anatomical key points from RGB images.	Camera vision based	Good accuracy on standard datasets. Good approach for independent living for elderly	OpenPose can be difficult to use	Standard dataset	RNN with LSTM