A Cloud Robotics Solution to Improve Social Assistive Robots for Active and Healthy Aging

The number of Europeans over 60 years of age will increase at a rate of two million per annum, while the working age population will shrink because of the low EU birthrate [1]. As a result, in 2060 there will be one retired senior for every two persons of working age (aged 15–64) [2]. The aging process causes a physiological decrease of the motor, sensory and cognitive abilities of the people who then may have trouble remembering, learning new things, concentrating or making decision about everyday life. Most of older are affected by one or several chronic diseases requiring several medicines and the periodic monitoring of their health parameters. This will increase the demand for nurse practitioners (+94 % in 2025) [3] and physician assistants (+72 % in 2025) [4] with several implications for the quality of care and for the configuration of future cost-effective care delivery systems. Furthermore, one in six of all 74 million elderly people now living in Europe is at risk of poverty [5] and the number of elderly persons living alone will increase more and more. Moreover most of the EU seniors want to remain in their familiar environment and to live as independently as possible [6], even if affected by age-related limitations.

At the same time, the social and economical sustainability of a safe and independent aging of the elderly is expected to one of the next post-crisis challenges. For this reason, an aging society could benefit fro the use of intelligent agents, such us smart homes and service robots, to assist in fulfilling the daily needs of the elderly [7‐10]. In Europe this approach is called ambient assisted living (AAL) [11] as the joint programme that funded and is funding several projects having this focus. According to several researches [9, 12, 13] the main needs of the elderly can be summarized as:As older people spend more time in their homes [6], smart living systems, sheltered houses and ’age-friendly’ environments could be fundamental tools to help seniors live independently, manage correctly their health care, delay/avoid institutionalization and stay active as long as possible. Robots could play a fundamental role in augmenting the utility and the efficience of the use of such technologies and services [7] because, in addition to their ability to provide physical support to older persons, they can also cooperate and interact with them [14‐16] thus facilitating their care and making the therapeutic process more enjoyable [17].

A social assistive robot for AAL can thus provide services of great utility for medication management, care and appointment reminders [18], monitoring of vital signs and the environment for user safety and security [7, 19], and can also improve seniors’ context awareness and situation recognition to help them and their caregivers in taking daily decisions.

However the success of these innovative service solutions greatly depends on the level of reliability and acceptability of these tools as perceived by elderly users. These aspects are crucial for the real deployment of these smart services in private homes and residential facilities in the near future. The acceptability of technological devices and services greatly depends on their utility, effectiveness, efficiency, reliability and ease of use as perceived by end users [20‐22]. Despite their technological complexity the robotic agents can enhance the effectiveness and the efficiency of the assistive services, while advanced human robot interaction (HRI), implementing communication strategies more similar to natural human ones (e.g. speech and gestures), would improve the comfort of use of the entire system. In order to execute effective assistive services and to adopt the most natural interaction approaches the robotic assistants need to consider and elaborate a great deal of environmental and contextual data. Booth the performance of the robots and their social behavior can be improved by the recently introduced cloud robotics paradigm [23, 24]. Cloud robotics was defined as the combination of cloud computing and robotics?. Thus Cloud robotics is not related to a specific type of robot, but to the way robots store information and access a base knowledge. As a matter of fact, cloud computing could give to robotic systems the opportunity to exploit user centered interfaces, computational capabilities, on-demand provisioning services and large data storage with minimum guaranteed QoS, scalability and flexibility. Cloud robotics is expected to affect on the acceptance of robotic services, enabling a new generation of smarter and cheaper robots compared to the classic stand-alone and networked robots.

From the technical point of view, the present paper describes a cloud robotic system for AAL implementing the Robot-as-a-Service (Raas) paradigm, including a service robot integrated with a number of smart agents which exploit the potentialities of the cloud to improve the capabilities of the system and consequently the service performance. In particular, this paper aims to improve the current state of the art in cloud robotics, by designing cloud on-demand AAL services, where the connected robot and smart environments cooperate to provide assistive location-based services. The system involves autonomous robots, smart environments and a cloud platform to automatically accomplish to the services required by the users. The autonomous robot was able to perform speck recognition using a wearable microphone on the users, to recognize the keywords associated to predefined service requests. Once a request was recognized, the robot retrieved from the proposed cloud system, all the useful information to reach the user and perform the service. The Ad-hoc services and the technologies were designed to leverage the use of the cloud in the AAL domain and get the assistive robotics more close to real cost-effective deployments, while respecting important AAL requirements in terms of dependability and acceptability. The RaaS design improves the traditional server applications at least for the following reasons:

Over the last years several stand-alone social and assistive robots have been developed to support the elderly and their caregivers in their daily activities. For instance the Giraff robot (ExCITE Project) [25] was developed to provide tele-presence services and to support elderly persons in communication activity, while the Hobbit robot [26] was designed to detect emergency situations, handle objects and performs activities to enable seniors to stay longer in their homes. Other robotic solutions have been designed to provide remote medical assistance in hospitals or private houses, such as for the robotic nursery assistant described by Hu et al. [27], the VGo robot from VGo communications (New Hampshire, USA) and the RP-VITA robot from InTouch Health (Callifornia, USA).

These stand-alone robots are in charge of the entire sensing, planning and performance of the tasks and usually required high computational capabilities and expensive technologies that make them unaffordable in delivering complex services.

In order to effectively and efficiently provide complex assistive services like object transportation and location-based services, several robotic platforms have been designed according to the the networked robotics paradigm [28].

Networked robotics leverages wireless sensor agents distributed in smart environments and wearable and personal devices to reinforce the sensing capability of the robot with other external information that could improve the efficiency of the robot’s planning and its cooperation with the end users. Sensors distributed in the environment can also improve the safety of the robots by providing the necessary information to avoid risky and dangerous unwanted robot–human interactions. A current limitation of service robots moving in unstructured environments, is their inability to detect humans out of their sensing range. This situation may occur whenever an individual is approaching the robot from a direction that is not covered by, for example, the cameras, laser range finders or ultrasonic sensors, which are often used to detect human, help the robot navigate and avoid obstacles. Similarly, a robot can’t detect the presence of humans beyond walls, wich increases the risk of accidents, for example when a robot turns a corner. The use of environmental sensors may improve the robot’s situation awareness by providing information about the presence of people in the robot’s surroundings, helping robots behave safely. In accordance with this approach, Arndt and Berns [29] investigated the current state of the art of networked robots, and in particular the PEIS-Project [30] and the AmICA smart environment, integrated with a companion robot [31]. They concluded that smart environments could be profitably used to shift some complexity away from the mobile machines to the smart environment without compromising the safety of the overall system. Furthermore, a smart environment could significantly reduce the time for service delivery, by providing the robot with information about the user’s position [31]. The opportunity of the early detection and the prevention of potentially unsafe interactions between robots and people by leveraging the use of smart environment was also highlighted by Cavallo [15]. The use of distributed or wearable sensors can also improve the usability and acceptance of assistive robots, by providing innovative human robot interfaces. Recently, some research has focused on the use of wearable brain machine interfaces (BMI) to provide assistive services to impaired users. The BMIs in [32] and [33] were used to control robots or smart homes by observing brain waves and interpreting the user’s will. In such systems the user wears cutaneous electrodes to measure the brain waves. Usually the BMI requires a so called calibration or training phase, where the user is asked to concentrate for example on specific actions, that will be related to specific control inputs for the BMI and the robotic agent. After the training, the BMI will recognize the user’s control inputs, with a success rate that is often less than 95 % even using commercial solutions as in [34]. After the calibration and training, the user will control the connected robots or devices by concentrating on the desired control. The major drawback of such interfaces lies in the low information transfer rate provided by brain waves, the obtrusiveness of the cutaneous electrodes, the need for training and the high level of concentration required to give commands to the BMI. For these reasons, the interface of smart environments and robots with wearable BMIs to assist seniors is still not widespread for AAL applications, where the dependability and acceptance of the interface is crucial.

Smart environments can thus be used to enhance the robots’ sensing and planning capabilities, improve the HRI, facilitate the tracking and monitoring of patients, and also allow for better and long-term daily activity recognition [35]. Furthermore the integration of robots in smart environments can provide new opportunities in the assessment of the dependability, acceptance and usability. External sensors can provide impartial and additional information respect to the use of traditional questionnaires or the on-board sensors like in [36]. Sensor networks can track the users positions before, during and after the interaction with robots, to better characterize the entire HRI process. Wearable sensors can extract data on the user stress level (like the heart rate, heart rate variability or skin conductance) during the interactions. Environmental sensors can give information on the environmental conditions that may affect for example the robot’s perception (vision, speech recognition) in the task execution like the lighting condition, the presence of people in the robot’s proximity, power outages or the acoustic noise level. These kind of information can be used to better characterize the robots’ dependability respect to environmental factors. Similarly, smart environments provided with indoor localization systems, can improve the self-localization or the navigation ability of robots respect to the current state of the art in [37]. An indoor localization system can provide the initial data on the robot’s position after a wake-up, a reset or a fault of the inner sensors or the navigation system. This information can simplify the procedure and reduce the time for the robot to locate itself in the environment, using the embedded sensors like laser scanners, camera or ultrasounds. In the literature, Smart environments provides several kinds of services either to robots and users, including:In the literature, the opportunity to know the position and pose of the users, as well as the environmental conditions where the robots and humans interact, were considered crucial information for implementing socially believable robot behaviors [39], improve the robot’s proxemity and the user’s comfort in using the robot [40, 41].

The information required to provide such services could come from wearable and environmental sensors as well as distributed intelligent agents such as are described in Table 1 according to the networked robotics paradigm and [35, 40].

Indoor user localization, in particular, is one of the most challenging requirements for the assistive robotic systems of today. Smart environments are considered an enabling technology to improve robot navigation, provide personal services directly to the users, reduce the time for service delivery and improve its safety in case of critical situations. This is because when a robot knows the users’ positions in a domestic environment (even out of its sensing range), it can efficiently and safely navigate toward them to provide the proper service to the proper user [15].

Furthermore, the opportunity to make a robot able to face the user with a proper pose and at a comfortable, safe and proper distance, can positively affect the HRI and the acceptance of the robot [7]. The ability of a robot to efficiently seek the user by exploiting an ambient intelligent infrastructure is still an open scientific challenge [42]. Some recent research in this field have been founded by the European Communitys 7th Framework Program (FP7/2007-2013), like the CompanionAble project [14], the GiraffPluss project [16], and the Astromobile experiment in the ECHORD project [43] [15]. The knowledge of user position also facilitates patients’ tracking and monitoring processes for better and long-term daily activity monitoring and allows the recognition of critical situation.

However recent research has mainly focused on the development of robotic solutions for home applications, in a one-robot–one-user interaction model. Very few robotic applications have dealt with the integration of a number of smart environments, users, and robots, to provide social and assistive services in different and heterogeneous environments. Even if recent assistive robots focused on the support of consumers in crowed shopping malls [44] and multi-floor wide buildings like the CoBot robots [45], most assistive robots for AAL applications are still designed to carry out services in a one-robot-one-house? or in a “one-robot–one-user” interaction model. This approach doesnt match the recent trends in housing and social services [46], where the cooperation between seniors, and the sharing of goods and services are expected to improve the sustainability of an aging population. One of the few projects in line with this concept is the European project Robot-Era (GA 288899), developing 3D robotic services for aging well, that are a plurality of assistive services implemented by mean of a multitude of cooperating robots integrated with smart environments and acting in heterogeneous spaces, such as homes, condominiums and urban environments [47].

The paucity of examples of social robots for AAL in a multi-user or multi-environment scenario could be due to the limited computing capabilities and insufficiency of these robots for continuously supporting daily activities [24]. Continuous care support indeed requires the ability to assist a number of users in a variety of heterogeneous environments, and thus (i) perform complex reasoning, (ii) store a huge amount of data, (iii) provide assistive services fluently and repeatably, and (iv) interact with humans in dynamic, complex and unstructured environments.

The novel cloud robotic paradigm can fit these requirements by extending the concept of networked robotics [23] and enable a new generation of socially believable robots. It has been defined as the combination of cloud computing and robotics, enabling the provisioning of on-demand robotic services to a greater extent that has been ever done before. The service providers can leverage the elasticity of the resources of the cloud to deliver robotics services to users on-demand, regardless of the number of agents and users involved.

The storage and computational resources of the cloud enable robots to offload computational capability and perform complex processing, share information about users and environments, training data and learning processes [48]. The cloud will provide the resources to efficiently perform challenging robotic services like object recognition and manipulation, as well as perform social navigation and advanced human–robot interactions. Today’s high throughput mobile communication technologies (e.g. Wi-Fi, Wi-Max, 3G and LTE) will ensure high speed and reliable data exchange for an high quality of the data transmission between the robotic agents and the cloud.

In this context, the present paper aims to take a step in the current state of the art, by designing a Robot-as-a-Service infrastructure, able to provide assistive user location-based services taking into account the requirements for AAL. The proposed RaaS system was designed to assist seniors in a multi-user and multi-environment perspective, using cloud services to improve the ability of the connected robots. No Wizard-of-Oz functionality was implemented, since the cloud was intended to extend the sensing and reasoning capability of the connected autonomous service robots. In order to make a preliminary assess of the system’s dependability, an experimental set-up was performed to evaluate its reliability and accuracy in delivering robotic location-based services.

The RaaS system was designed to be scalable and fit the requirements for providing robotic services either in the home or in nursery-home environments, in a multi-user and multi-environment vision. It comprises hardware smart agents distributed over heterogeneous remote physical environments and software agents in a cloud platform as in Fig. 1. The hardware was selected to provide physical and cognitive support to the users and the proper user interfaces for system management and control. In particular, smart environments were instrumented with distributed and wearable sensors to extract as much information as possible on users’ positions and the status of the environments. A robot was integrated to provide physical and cognitive support to the users and exploit the robot’s embodiment to improve service acceptance. Data storage and processing were performed by software modules in the cloud, as well as the GUI for the system management and control.

From the caregiver’s point of view, the cloud robotic services were designed to simultaneously manage more seniors at the same time, regardless to the time and the location of the seniors. As seen in Table 1, a number of sensors were selected to provide the data to improve the performance and the social behavior of the robots. The selected sensors could be used for home monitoring, critical situation recognition for safe and secure living as well for determining users’ positions to improve the performance of location-based robotic services (e.g. drug delivery and medication reminder services).

Tables 2 and 3 present two possible examples of application scenarios (medication management and the recognition of critical situations) showing the service scheduling and the role of the agents of the RaaS system.

This section presents the preliminary experimentation performed to test the reliability of the DBMS module and assess the performance of the ULM in terms of user localization accuracy. The two cloud modules had different natures and thus they were tested according to different experimental protocols and specific metrics were selected. The experimentation was performed in two remote pilot sites to assess the performance of the DBMS and the ULM agents in a multi-environment context.

The quality of the assistive services provided by the proposed solution was assessed for each experimental site, computing:

In particular, the RTT was computed as the mean time over 24 h, and in order to take into account the varying use of bandwidth over the day, the RTT was also computed at night (8 h) and during the work-day (10 h). As shown in Table 5. The mean RTT in Domocasa was 40 ms, while for the Swedish site the RTT was 134.57 ms. The localhost RTT acquired during the experimentation was 7.46 ms, and was used as a benchmark. The RTT night data was computed from midnight to 8 a.m., while RTT day data was computed from 8 a.m. to 6 p.m. The Angen site exhibited a lower service responsiveness (higher service RTT), since the remote server for user localization was placed in Italy, at a distance of about 2000 km. The DL value was computed as the ratio between the number of successfully addressed requests for user position and the total number of requests. In particular, a request for user position was sent at the rate of 1 Hz to the DBMS to simulate the call of a number of robotic services from several users. The number of service fails was less than 0.5 % in Italy, and 0.002 % for the Angen site. This result demonstrated that a high service reliability could be achieved even monitoring very far environments.

For each point of interest in the user trajectory in the Domocasa and Angen sites, the mean localization error and the error variance were computed, as shown in Fig. 6. In Domocasa and Angen, the mean absolute localization errors were respectively 0.98 and 0.79 m, while the root mean square errors were respectively 1.22 and 0.89 m. The standard deviation of the absolute errors was 0.57 m in Domocasa and 0.47 m in Angen. On average, the absolute localization error considering the two setups was 0.89 m, and the RMSE was 1.1 m. The localization error and its standard deviation depend on the environment and the number of installed sensors. Indeed the localization accuracy was different for each monitored room. In particular, the presence of electromagnetic reflective surfaces in each room creates stationary waves that affect the accuracy of the RSS based localization systems. Furthermore, the number \((\mathrm{device/m^2})\) of installed anchors or presence sensors affects the overall accuracy of the system. The more the localization sensors are installed in a room, the finer could be the accuracy of the localization service.

The results demonstrated that the proposed localization system was able to locate several users in remote environments, with an appropriate in-room resolution. Indeed, for AAL applications, a meter-level localization accuracy has been considered sufficient to deliver assistive services to users [35].

The opportunity to get data from different kinds of sensors, like anchors for the observation of the RSS from mobile nodes and traditional presence sensors, positively affected the performance of the localization system, improving the accuracy in specific areas of interest. The advantage in the use of presence sensors to enhance the localization accuracy of the system, was measured as the reduction of the localization errors, using the switch at the kitchen door and the pressure sensor on the chair in the kitchen and on the sofa in the Angen site as shown in Table 4. The user position was estimated in two different experimental trials, with and without the presence sensors connected to the SNet. The use of the presence sensors increased the localization accuracy in the selected positions by an average of 35 %.

The proposed work demonstrated the feasibility of the proposed cloud robotic solution for the provision of location based and personalized assistive services to seniors in relevant environments including an home and a care facility environment. The tests on the reliability and responsiveness of the system, demonstrated its ability to provide location based services to remote sites (2000 km) with a mean delay time of less than 134.57 ms and a data loss of less than 0.5 %, which can be considered sufficient for AAL applications. The reduced amount of time spent for the processing and the providing of information to the connected robotic agents make it possible to image the use of a single cloud infrastructure for the management of a number of connected agents and the provisioning of assistive services to an extent of users. This impact positively on the social sustainability of cloud robotics for the provisioning of services to the ageing population. A series of novelties were introduced also to get a step forward in the state of the art in the indoor localization of users, to tackle the challenge of providing location based services in scenarios featured by the possible presence of a plurality of users sharing the same environment. In particular the localization system made use of sectorial antennas to spot specific areas of interest for the identification and the localization of the mobile radios worn by the users. Furthermore the use of ZigBee radios allowed the development of self-healing mesh networks and the performing of multi-hop message routing. This kind of networking solution enabled the connected sensors to exchange data for the users localization and the context monitoring both in homes or multy-floor buildings improving the molecularity of the solution and the dependability of the wireless radio links. The proposed sensor fusion algorithm improved the localization performances by using data from different typology of sensors and localization techniques. The sensor fusion was intended to improve the system tolerance to hardware faults and minimize the impact of installation mistakes and calibration issues on the system accuracy. The proposed localization service achieved a meter level accuracy in locating people that was sufficient to address robots to the users and provide assistive services [35] like a drug or a medication remind. The ability to distinguish between users sharing the same environment, enabled the system to deliver personalized services in homes with more than one inhabitant or in more relevant environments like care facilities. Thanks to this technology the robot was also enabled to recognize and roughly locate users without using cameras, complying with privacy issues and enhancing the system acceptability. The cloud database provided a shared base of knowledge about the users positions, the environment and the robot status, and acted as a blackboard where the connected smart agents can set or get the data for continually deliver assistive services. New robotic agents, smart environments or single sensors could be easily integrated in the system, by means of an internet connection and the use of a compatible communication protocol improving the scalability and the maintainability of the proposed system. Two possible examples of application scenarios were discussed in Sect. 3 more complex services could be designed based on the proposed RaaS architecture. The cloud features, including the scalable computing and storage resources are opening new kind of opportunities for the service robotics. The outsourcing of the computational resources allows to design cheaper and lighter robots more suitable for the market. Furthermore, the ability to share relevant and a relevant amount of information between the robots and the connected smart agents would improve the robots’ context awareness and their ability to provide advanced services to a plurality of users. The opportunity to drive a robot to a specific user among the others and provide a dedicated assistance (medication, health status assessment, walk support) based on the user preference or needs would also improve the utility and effectiveness of system. A cloud connected robot would also be able to show new behaviours or implement new functionality by simply receiving instruction from the cloud without the need to reprogram the platform, thereby improving the robot adaptability and the service customization. More complex assistive services could be imaged based on the proposed system, where for example the user asks for a walk or a stand support, or the transportation of objects. To do this, a more complex human–robot integration would be provided, and the localization system would achieve an higher resolution. The localization accuracy could be improved by installing more anchors in the environments, or introducing new types of sensors into the sensor fusion algorithm of the ULM. For example, the use of Ultra Wide Band (UWB) devices for locating mobile devices measuring the time of arrival of electromagnetic waves seems promising. Nevertheless, the localization accuracy is not the only parameter to take into account when designing an indoor localization system for AAL. The low form factor of the wearable devices, the ability to work for an entire day without the need for battery replacement and the ability to monitor wide environments using a single sensor network to avoid the disconnection of the devices while moving, are important features that affects the usability of the localization technologies. Further research will concern the improvement of the localization system, by introducing an Inertial Measurement unit (IMU) into the wearable device. The IMU would be useful to estimate the orientation of the user in the environment to improve the quality of the human robot interaction. Indeed, thanks to this information the robot would improve its proxemics computing the proper trajectory, the orientation and the distance to interact with the user and provide the service in a socially believable manner.

When it comes to AAL, the dependability of the technologies is crucial and the use of wireless broadband could negatively impact on the reliability, availability and responsiveness of cloud based services by introducing delays or data loss. Further investigations will be focused on the selection of the proper communication technologies (e.g. LTE, 3G...) and the strategies to automatically assess and restore the dependability of the data exchange. For example, a possible recovery actions in case of performance degradation would be the switching to different communication technologies or the reduction of the data flow by performing some pre-processing on the robotic agents. Specific investigations will be focused on the improvement of the human–robot interaction and thee definition of advanced human–robot interaction models based on a extended sensor fusion approach and machine learning algorithms in the cloud, to improve the acceptability of the personalized robotic services.